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Keywords = semi-active suspension

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15 pages, 2994 KiB  
Article
New Mixed Skyhook and Displacement–Velocity Control for Improving the Effectiveness of Vibration Isolation in the Lateral Suspension System of a Railway Vehicle
by Yaojung Shiao and Tan-Linh Huynh
Appl. Sci. 2024, 14(24), 11680; https://doi.org/10.3390/app142411680 - 14 Dec 2024
Viewed by 299
Abstract
Demands for increasing the velocity and load carrying capacity of railway vehicles are a challenge to the passive suspension systems used for isolating the lateral vibrations of the carbody of a railway vehicle, especially under a wide range of vibration frequencies. Semiactive suspension [...] Read more.
Demands for increasing the velocity and load carrying capacity of railway vehicles are a challenge to the passive suspension systems used for isolating the lateral vibrations of the carbody of a railway vehicle, especially under a wide range of vibration frequencies. Semiactive suspension systems, especially systems with a magnetorheological damper (MRD), have been investigated as promising alternatives. Many control algorithms have been developed for fine-tuning the damping force generated by MRDs, but they have been ineffective in isolating carbody vibrations at or around the resonance frequencies of the carbody and bogie. This study aims to develop a mixed control algorithm for a new skyhook (SH) control and a new displacement–velocity (DV) control to improve the effectiveness of vibration isolation in resonance frequency regions while producing high performance across the remaining frequencies. The damping coefficient of the new SH controller depends on the vibration velocity of the components of the suspension system and the skyhook damping variable, whereas that of the new DV controller depends on the velocity and displacement of the components of the suspension system and the stiffness variable. The values of the skyhook damping variable and stiffness variable were identified from the vibration velocity of the carbody using the trial and error method. The results of a numerical simulation problem indicated that the proposed control method worked effectively at low frequencies, similar to the conventional SH–DV controller, whereas it significantly improved ride comfort at high frequencies; at the resonance frequency of the bogie (14.6 Hz), in particular, it reduced the vibration velocity and acceleration of the carbody by 50.85% and 45.39%, respectively, compared with the conventional mixed SH–DV controller. The simplicity and high performance of the new mixed SH–DV control algorithm makes it a promising tool to be applied to the semiactive suspension of railway vehicles in real-world applications. Full article
(This article belongs to the Special Issue Novel Advances in Noise and Vibration Control)
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<p>The quarter railway vehicle model.</p>
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<p>(<b>a</b>) The velocity region division and (<b>b</b>) the contribution coefficient for the new SH control under the vibration velocity of the carbody. (<b>a</b>): the regions (1) 0 to 0.0075 m/s, (2) 0.0075 to 0.025 m/s, (3) 0.025 to 0.1 m/s, and (4) ≥0.1 m/s, assumed to correspond the carbody vibration at the high frequency domain, medium frequency domain, around the second-order resonance frequency domain, and around the first-order resonance frequency domain, respectively. A, C, D, E, G, and H are the intersections of region thresholds and line graph of vibration velocity of carbody; B and F are the peak at the first and second-order resonance frequencies, respectively.</p>
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<p>Comparisons of the vibration transmissibility under different controls.</p>
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<p>The RMS of (<b>a</b>) displacement, (<b>b</b>) velocity, and (<b>c</b>) acceleration of the carbody.</p>
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<p>Damping contribution coefficient for the new DV control according to the vibration velocity of the carbody.</p>
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<p>Comparisons of the vibration transmissibility of different controls under sinusoidal signal input with amplitudes of ±5 mm.</p>
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<p>The RMS of (<b>a</b>) displacement, (<b>b</b>) velocity, and (<b>c</b>) acceleration of the carbody under sinusoidal signal input with amplitudes of ±5 mm.</p>
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<p>Comparisons of the vibration transmissibility of the carbody under three control algorithms under sinusoidal signal input with amplitudes of ±5 mm.</p>
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<p>The RMS of (<b>a</b>) displacement, (<b>b</b>) velocity, and (<b>c</b>) acceleration of the carbody under sinusoidal signal input with amplitudes of ±5 mm.</p>
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21 pages, 6132 KiB  
Article
Self-Sensing Approach for Semi-Active Control of Variable Damping Electromagnetic Suspension System
by Chao Fu, Pengfei Liu, Jianqiang Yu, An Qin and Donghong Ning
Actuators 2024, 13(12), 480; https://doi.org/10.3390/act13120480 - 27 Nov 2024
Viewed by 384
Abstract
This paper combines the Kalman filter observer with self-sensing technology and integrates it into the electromagnetic damper (EMD), estimating the displacement and velocity of the EMD based on the three-phase voltage generated by the permanent magnet synchronous motor (PMSM). The self-sensing performance of [...] Read more.
This paper combines the Kalman filter observer with self-sensing technology and integrates it into the electromagnetic damper (EMD), estimating the displacement and velocity of the EMD based on the three-phase voltage generated by the permanent magnet synchronous motor (PMSM). The self-sensing performance of the EMD is verified through theoretical analysis and experimental results. A vehicle suspension vibration control system composed of one-quarter vehicle electromagnetic suspension (EMS), a acceleration damping driven control (ADDC) algorithm, and a vibration excitation platform is established to test the vibration control performance of the self-sensing EMS. The experimental results show that under random road excitation, compared to passive suspension, the self-sensing-based ADDC reduced the vehicle vertical acceleration of the vehicle suspension, with a 28.92% decrease in the root mean square (RMS) value of the vehicle vertical acceleration. This verifies the effectiveness of the self-sensing capability of the EMS system. Incorporating self-sensing technology into the EMS system improves the vibration reduction performance of the suspension. Full article
(This article belongs to the Special Issue Modeling and Control for Chassis Devices in Electric Vehicles)
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<p>EMD suspension system structure.</p>
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<p>Equivalent principle of the PMSM circuit.</p>
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<p>Characteristic testing of EMD.</p>
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<p>Characteristic curves of EMD: (<b>a</b>) force-displacement characteristic curves; (<b>b</b>) force-velocity characteristic curves.</p>
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<p>(<b>a</b>) Voltage variation curves; (<b>b</b>) current variation curves.</p>
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<p>A/B phase pulse: (<b>a</b>) phase A leads phase B; (<b>b</b>) phase B leads phase A.</p>
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<p>Self-sensing working principle.</p>
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<p>Voltage pulse signal of the stationary coordinate system.</p>
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<p>Comparison between estimated value and actual value under sine excitation: (<b>a</b>) displacement; (<b>b</b>) velocity.</p>
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<p>Self-sensing variable damping EMD suspension system.</p>
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<p>Comparison between estimated value and actual value under sine excitation: (<b>a</b>) 10 mm 1.5 Hz low-frequency sine; (<b>b</b>) 1 mm 10 Hz high-frequency sine.</p>
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<p>Comparison between estimated value and actual value.</p>
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<p>One-quarter vehicle 2-degree-of-freedom suspension system model: (<b>a</b>) ideal acceleration damping suspension; (<b>b</b>) EMS.</p>
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<p>The control logic of the VD-EMS.</p>
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<p>EMS test system.</p>
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<p>Comparison between estimated value and actual value under sine excitation.</p>
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<p>Force track performance under sine excitation.</p>
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<p>Vehicle vertical acceleration under sine excitation.</p>
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<p>Suspension travel under sine excitation.</p>
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<p>Comparison between estimated value and actual value under random road excitation.</p>
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<p>Force track performance under random road excitation.</p>
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<p>Vehicle vertical acceleration under random road excitation.</p>
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<p>Suspension travel under random road excitation.</p>
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<p>Evaluation parameters of vehicle vertical acceleration.</p>
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17 pages, 18738 KiB  
Article
Three-Axis Vibration Isolation of a Full-Scale Magnetorheological Seat Suspension
by Young T. Choi, Norman M. Wereley and Gregory J. Hiemenz
Micromachines 2024, 15(12), 1417; https://doi.org/10.3390/mi15121417 - 26 Nov 2024
Viewed by 552
Abstract
This study examines the three-axis vibration isolation capabilities of a full-scale magnetorheological (MR) seat suspension system utilizing experimental methods to assess performance under both single-axis and simultaneous three-axis input conditions. To achieve this, a semi-active MR seat damper was designed and manufactured to [...] Read more.
This study examines the three-axis vibration isolation capabilities of a full-scale magnetorheological (MR) seat suspension system utilizing experimental methods to assess performance under both single-axis and simultaneous three-axis input conditions. To achieve this, a semi-active MR seat damper was designed and manufactured to address excitations in all three axes. The damper effectiveness was tested experimentally for axial and lateral motions, focusing on dynamic stiffness and loss factor using an MTS machine. Prior to creating the full-scale MR seat suspension, a scaled-down version at one-third size was developed to verify the damper’s ability to effectively reduce vibrations in response to practical excitation levels. Additionally, a narrow-band frequency-shaped semi-active control (NFSSC) algorithm was developed to optimize vibration suppression. Ultimately, a full-scale MR seat suspension was assembled and tested with a 50th percentile male dummy, and comprehensive three-axis vibration isolation tests were conducted on a hydraulic multi-axis simulation table (MAST) for both individual inputs over a frequency range up to 200 Hz and for simultaneous multi-directional inputs. The experimental results demonstrated the effectiveness of the full-scale MR seat suspension in reducing seat vibrations. Full article
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<p>The multi-axis magnetorheological (MR) seat damper can be applied to either ground or air vehicle seat suspensions. (<b>a</b>) Schematic diagram and (<b>b</b>) fabricated seat damper.</p>
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<p>Experimental setup used to measure the damping performance of the single MR seat damper on an MTS machine. (<b>a</b>) Axial direction and (<b>b</b>) lateral direction.</p>
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<p>Axial dynamic stiffness and loss angle of the multi-axis MR seat damper under ±1.0 mm excitation displacement. Note that the initial axial compression was 2 mm.</p>
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<p>Lateral dynamic stiffness and loss angle of the multi-axis MR seat damper under ±1.0 mm excitation displacement. Note that the initial axial compression was 2 mm.</p>
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<p>The single-degree-of-freedom (DOF) testing stand for the 1/3 scale MR seat suspension for the axial (i.e., vertical) direction: (<b>a</b>) test stand, (<b>b</b>) controller box.</p>
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<p>Desired control input shape of the narrow-band frequency-shaped semi-active control (NFSSC) algorithm in the frequency domain.</p>
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<p>Transmissibility of the 1/3rd scale MR seat suspension for the axial direction (excitation ±0.1 g).</p>
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<p>Test configuration of the full-scale MR seat suspension: (<b>a</b>) full-scale MR seat suspension, (<b>b</b>) MR seat damper configuration (top view).</p>
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<p>Three-axis transmissibility at the seat pan of the full-scale MR seat suspension using the NFSSC control algorithm for each directional excitation input (excitation level: ±0.1 g): (<b>a</b>) <span class="html-italic">x</span>-axis excitation input, (<b>b</b>) <span class="html-italic">y</span>-axis excitation input, (<b>c</b>) <span class="html-italic">z</span>-axis excitation input.</p>
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<p>RMS transmissibility of the full-scale MR seat suspension using the NFSSC algorithm fo excitation input in each direction: (<b>a</b>) for the relatively low-frequency range (3–20 Hz), (<b>b</b>) for the higher frequency range (20–200 Hz).</p>
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<p>Measured time profiles of representative transient inputs for seat suspensions in a military propeller aircraft [<a href="#B29-micromachines-15-01417" class="html-bibr">29</a>].</p>
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<p>Measured time responses at the seat pan of the full-scale MR seat suspension using the NFSSC algorithm for each directional representative excitation input (<b>a</b>) for <span class="html-italic">x</span>-axis excitation input, (<b>b</b>) for <span class="html-italic">y</span>-axis excitation input, and (<b>c</b>) for <span class="html-italic">z</span>-axis excitation input.</p>
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<p>RMS accelerations of the full-scale MR seat suspension under the NFSSC algorithm (<b>a</b>) for each directional excitation input and (<b>b</b>) for simultaneous three-axis excitation inputs.</p>
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<p>Overall RMS accelerations at the seatback of the full-scale MR seat suspension under the NFSSC algorithm (<b>a</b>) for each directional excitation input and (<b>b</b>) for simultaneous three-axis excitation inputs.</p>
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18 pages, 15595 KiB  
Article
Vehicle Attitude Control of Magnetorheological Semi-Active Suspension Based on Multi-Objective Intelligent Optimization Algorithm
by Kailiang Han, Yiming Hu, Dequan Zeng, Yinquan Yu, Lei Xiao, Jinwen Yang, Weidong Liu and Letian Gao
Actuators 2024, 13(12), 466; https://doi.org/10.3390/act13120466 - 21 Nov 2024
Viewed by 320
Abstract
A multi-objective intelligent optimization algorithm-based attitude control strategy for magnetorheological semi-active suspension is proposed to address the vehicle attitude imbalance generated during steering and braking. Firstly, the mechanical properties of the magnetorheological damper (MRD) are tested, and the parameters in the hyperbolic tangent [...] Read more.
A multi-objective intelligent optimization algorithm-based attitude control strategy for magnetorheological semi-active suspension is proposed to address the vehicle attitude imbalance generated during steering and braking. Firstly, the mechanical properties of the magnetorheological damper (MRD) are tested, and the parameters in the hyperbolic tangent model of the magnetorheological damper are identified through experiments. Secondly, a simulation model of the whole vehicle multi-degree-of-freedom vehicle dynamics including magnetorheological damper is established, and the whole-vehicle Linear Quadratic Regulator (LQR) controller is designed. Then, the optimization design model of the joint vehicle controller and vehicle dynamics is established to design the optimization fitness function oriented to the body attitude control performance, and the attitude optimal controller is calculated with the help of multi-objective intelligent optimization algorithm. Simulation results show that the proposed control method is able to improve the body roll angle, body pitch angle, and suspension dynamic deflection well on the basis of ensuring no deterioration in other performance indexes, ensuring good attitude control capability of the vehicle and verifying the feasibility of the control strategy. Full article
(This article belongs to the Special Issue Magnetorheological Actuators and Dampers)
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<p>a1-I curve.</p>
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<p>Damping force–displacement curve.</p>
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<p>Damping force–velocity curve.</p>
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<p>Multi-degree-of-freedom dynamics model.</p>
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<p>Flowchart of multi-objective particle swarm algorithm.</p>
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<p>Pareto front viewable view.</p>
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<p>Dominance value of each solution.</p>
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<p>Damping force comparison: (<b>a</b>) damping force of the left front tire; (<b>b</b>) damping force of the left rear tire; (<b>c</b>) damping force of the right front tire; and (<b>d</b>) damping force of the right rear tire.</p>
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<p>Time domain response curve of body attitude: (<b>a</b>) body roll angle; (<b>b</b>) body pitch angle; (<b>c</b>) left front suspension deflection; (<b>d</b>) left rear suspension deflection; (<b>e</b>) right front suspension deflection; and (<b>f</b>) right rear suspension deflection.</p>
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<p>Time domain response curves of vertical acceleration and tire dynamic deformation: (<b>a</b>) body vertical acceleration; (<b>b</b>) dynamic deformation of the left front tire; (<b>c</b>) dynamic deformation of the left rear tire; (<b>d</b>) dynamic deformation of the right front tire; and (<b>e</b>) dynamic deformation of the right rear tire.</p>
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<p>Frequency domain curve of road input against typical output: (<b>a</b>) road input to body vertical acceleration; (<b>b</b>) road input to suspension dynamic deflection; and (<b>c</b>) road input to tire dynamic deformation.</p>
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<p>Frequency domain curve of lateral acceleration against typical outputs: (<b>a</b>) lateral acceleration to body roll angle; (<b>b</b>) lateral acceleration to suspension dynamic deflection; and (<b>c</b>) lateral acceleration to tire dynamic deformation.</p>
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<p>Frequency domain curves of longitudinal acceleration against typical outputs: (<b>a</b>) longitudinal acceleration to body pitch angle; (<b>b</b>) longitudinal acceleration to suspension deflection; and (<b>c</b>) longitudinal acceleration to tire dynamic deformation.</p>
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17 pages, 4598 KiB  
Article
Establishment of Betalain-Producing Cell Line and Optimization of Pigment Production in Cell Suspension Cultures of Celosia argentea var. plumosa
by Thapagorn Sang A Roon, Poramaporn Klanrit, Poramate Klanrit, Pornthap Thanonkeo, Jirawan Apiraksakorn, Sudarat Thanonkeo and Preekamol Klanrit
Plants 2024, 13(22), 3225; https://doi.org/10.3390/plants13223225 - 16 Nov 2024
Viewed by 684
Abstract
The prevalence of synthetic colorants in commercial products has raised concerns regarding potential risks, including allergic reactions and carcinogenesis, associated with their use or consumption. Natural plant extracts have gained attention as potential alternatives. This research focuses on callus induction and the establishment [...] Read more.
The prevalence of synthetic colorants in commercial products has raised concerns regarding potential risks, including allergic reactions and carcinogenesis, associated with their use or consumption. Natural plant extracts have gained attention as potential alternatives. This research focuses on callus induction and the establishment of cell suspension cultures from Celosia argentea var. plumosa. Friable callus was successfully induced using hypocotyl explants cultured on semi-solid Murashige and Skoog (MS) medium supplemented with 1 mg/L 2,4-dichlorophenoxyacetic acid (2,4-D) and 0.1 mg/L 6-benzylaminopurine (BAP). The friable callus cell line was used to establish a suspension culture. The effects of sucrose, BAP, and tyrosine concentrations on betalain production were investigated using response surface methodology (RSM) based on central composite design (CCD). Optimal conditions (43.88 g/L sucrose, 0.15 mg/L tyrosine, and 0.77 mg/L BAP) yielded 43.87 mg/L total betalain content after 21 days, representing a threefold increase compared to the control. BAP had a significant positive impact on betalain production, and increasing BAP and sucrose concentrations generally led to higher betalain production. However, tyrosine was not a significant factor for betalain production in cell suspension cultures. Additionally, antioxidant assays showed that suspension-cultured cells (SCCs) under optimized conditions exhibited free radical scavenging activity comparable to that observed in C. argentea var. plumosa flower extract. This study indicates the potential for further research on betalain production using C. argentea var. plumosa cell cultures, which may have commercial applications. Full article
(This article belongs to the Section Plant Development and Morphogenesis)
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<p><span class="html-italic">C. argentea</span> var. <span class="html-italic">plumosa</span> plant and callus cultures: (<b>A</b>) <span class="html-italic">C. argentea</span> var. <span class="html-italic">plumosa</span> with red inflorescence; (<b>B</b>) 30-day-old seedlings; (<b>C</b>) callus initiation from explants after 2 weeks; (<b>D</b>) callus growth on callus induction medium, CIM (Murashige and Skoog (MS) medium supplemented with 1 mg/L 2,4-dichlorophenoxyacetic acid (2,4-D) and 0.1 mg/L 6-benzylaminopurine (BAP)) after 4 weeks; (<b>E</b>) proliferation of different callus colors on CIM after 15 days of starting new subculture; (<b>F</b>) red callus proliferation on CIM after 15 days of starting new subculture.</p>
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<p>Cell suspension cultures of <span class="html-italic">C. argentea</span> var. <span class="html-italic">plumosa</span>: (<b>A</b>) friable callus from the red cell line; (<b>B</b>) suspension-cultured cells (SCCs) at 15 days of culture; (<b>C</b>) bright-field microscopy of 15-day friable pigmented callus; (<b>D</b>) bright-field microscopy of non-pigmented cells; (<b>E</b>–<b>G</b>) SCCs stained with Hoechst 33342 under fluorescent microscopy to visualize nuclei in different channels, the images were captured in the same field of view; (<b>E</b>) cells under RGB bright-field; (<b>F</b>) cells under fluorescent filter (excitation: 357 nm/emission: 447 nm); (<b>G</b>) merged RGB bright-field and fluorescence images. Arrows indicate nuclear positions, magnification = 200×, scale bar = 150 μm.</p>
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<p>Three-dimensional response surface plots showing the effect of (<b>A</b>) sucrose, (<b>B</b>) BAP, and (<b>C</b>) tyrosine on betalain content.</p>
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<p>Growth profile and betalain production in <span class="html-italic">C. argentea</span> var. <span class="html-italic">plumosa</span> cell suspension cultures under optimized conditions. Bars indicate mean ± SD from triplicate experiments (n = 3).</p>
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<p>Antioxidant capacity of the SCC extracts of <span class="html-italic">C. argentea</span> var. <span class="html-italic">plumosa</span> determined by ABTS and DPPH assays. Data are presented as the means ± SD of the results of triplicate determinations. Different superscript letters within each assay indicate significant differences between samples (<span class="html-italic">p</span> ≤ 0.05) as determined by Duncan’s Multiple Range Test (DMRT). Control, the SCC extract from cells cultured under unoptimized conditions); SCC, the SCC extract from cells cultured under optimized conditions; Flower, the inflorescence extract, and Vitamin C (ascorbic acid).</p>
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17 pages, 5064 KiB  
Article
Robust Static Output Feedback Control of a Semi-Active Vehicle Suspension Based on Magnetorheological Dampers
by Fernando Viadero-Monasterio, Miguel Meléndez-Useros, Manuel Jiménez-Salas and Beatriz López Boada
Appl. Sci. 2024, 14(22), 10336; https://doi.org/10.3390/app142210336 - 10 Nov 2024
Viewed by 627
Abstract
This paper proposes a novel design method for a magnetorheological (MR) damper-based semi-active suspension system. An improved MR damper model that accurately describes the hysteretic nature and effect of the applied current is presented. Given the unfeasibility of installing sensors for all vehicle [...] Read more.
This paper proposes a novel design method for a magnetorheological (MR) damper-based semi-active suspension system. An improved MR damper model that accurately describes the hysteretic nature and effect of the applied current is presented. Given the unfeasibility of installing sensors for all vehicle states, an MR damper current controller that only considers the suspension deflection and deflection rate is proposed. A linear matrix inequality problem is formulated to design the current controller, with the objective of enhancing ride safety and comfort while guaranteeing vehicle stability and robustness against any road disturbance. A series of experiments demonstrates the enhanced performance of the proposed MR damper model, which exhibits greater accuracy than other state-of-the-art damper models, such as Bingham or bi-viscous. An evaluation of the vehicle behavior under two simulated road scenarios has been conducted to demonstrate the performance of the proposed output feedback MR damper-based semi-active suspension system. Full article
(This article belongs to the Special Issue Advances in Vehicle System Dynamics and Control)
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<p>Common schematic of an MR damper.</p>
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<p>A typical curve of the proposed model (<b>a</b>) Suspension deflection vs. Damper force, (<b>b</b>) Suspension deflection rate vs. Damper force.</p>
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<p>Semi-active quarter-car suspension model.</p>
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<p>Experimental MR damper setup.</p>
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<p>Experimental and estimated MR damper curves for an amplitude of the piston displacement of 15 mm and different levels of applied current at a frequency of excitation of 3 Hz: (<b>a</b>) Deflection vs. Force, (<b>b</b>) Deflection Rate vs. Force, (<b>c</b>) Estimation Error, (<b>d</b>) Correlation.</p>
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<p>Experimental and estimated MR damper curves for an amplitude of the piston displacement of 20 mm and different levels of applied current at a frequency of excitation of 6 Hz: (<b>a</b>) Deflection vs. Force, (<b>b</b>) Deflection Rate vs. Force, (<b>c</b>) Estimation Error, (<b>d</b>) Correlation.</p>
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<p>Experimental and estimated MR damper curves for an amplitude of the piston displacement of 20 mm and a current of 1.8 A at a frequency of excitation of 6 Hz: (<b>a</b>) Deflection vs. Force, (<b>b</b>) Deflection Rate vs. Force, (<b>c</b>) Estimation Error, (<b>d</b>) Correlation.</p>
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<p>The architectural framework of the simulation.</p>
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<p>Simulated road profiles: (<b>a</b>) Road bump, (<b>b</b>) Class A road according to ISO 8608.</p>
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<p>Simulation results under a road bump: (<b>a</b>) Vertical acceleration, (<b>b</b>) Vertical acceleration PSD, (<b>c</b>) Suspension deflection, (<b>d</b>) Normalized dynamic tire load, (<b>e</b>) Input force, (<b>f</b>) MR damper current.</p>
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<p>Simulation results under a Grade A road: (<b>a</b>) Vertical acceleration, (<b>b</b>) Vertical acceleration PSD, (<b>c</b>) Suspension deflection, (<b>d</b>) Normalized dynamic tire load, (<b>e</b>) Input force, (<b>f</b>) MR damper current.</p>
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22 pages, 7344 KiB  
Article
Performance Verification and Evaluation of Semi-Active Actuator System Using Quarter Car Lab Simulation with RLDA Data
by Jeongwoo Lee and Jaepoong Lee
Appl. Sci. 2024, 14(22), 10093; https://doi.org/10.3390/app142210093 - 5 Nov 2024
Viewed by 603
Abstract
This study outlines a methodology for determining the durability specifications of electronically controlled dampers by examining performance degradation observed on actual driving roads. It identifies areas of performance decline and the primary causes affecting major actuator dampers. Traditionally, the durability performance of automobile [...] Read more.
This study outlines a methodology for determining the durability specifications of electronically controlled dampers by examining performance degradation observed on actual driving roads. It identifies areas of performance decline and the primary causes affecting major actuator dampers. Traditionally, the durability performance of automobile parts has been assessed by calculating damage based on load profiles. However, analyzing actual road conditions is essential because the control commands of electronically controlled suspension systems change in real time according to load conditions. Simulations based on Road Load Data Acquisition (RLDA) use statistically independent representative road surfaces to assess damper deterioration performance. Following this analysis, a rig test of the damper is performed to establish the durability specifications for damper actuator products. The primary form of performance degradation observed was a change in the tensile damping force, which was more substantial than the degradation observed on the compression side. Oil leakage and cavitation were identified as significant influencing factors from a Failure Mode and Effects Analysis (FMEA) perspective. The study concludes that additional design research is necessary, focusing on damper oil and leakage, while also considering the control algorithm’s effects in designing electronically controlled dampers. Full article
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<p>Continuous damping control system configuration.</p>
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<p>Brief CDC system control functions.</p>
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<p>Working principle of CDC system point of damping force variations.</p>
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<p>CDC damper’s internal hydraulic components and flow: (<b>a</b>) CDC damper’s assembly and flow; (<b>b</b>) fluid flow and control current; and (<b>c</b>) damper assembly (variable valve integration).</p>
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<p>CDC system’s damping force creation flow diagram (in detail).</p>
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<p>Data acquisition system for the motion detection of the damper.</p>
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<p>Equivalent amplitude of front and rear damper with respect to road conditions.</p>
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<p>Rain flow matrix of the damper velocity (front): (<b>a</b>) Highway; (<b>b</b>) Country road; (<b>c</b>) City road; (<b>d</b>) Unpaved road.</p>
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<p>Rain flow matrix of the damper velocity (rear): (<b>a</b>) Highway; (<b>b</b>) Country road; (<b>c</b>) City road; (<b>d</b>) Unpaved road.</p>
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<p>Quarter car simulator.</p>
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<p>RLDA: (<b>a</b>) combination of country/highway/city/unpaved; (<b>b</b>) country road; (<b>c</b>) highway; (<b>d</b>) city road (general case); (<b>e</b>) city road (construction area); (<b>f</b>) unpaved road.</p>
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<p>RLDA: (<b>a</b>) combination of country/highway/city/unpaved; (<b>b</b>) country road; (<b>c</b>) highway; (<b>d</b>) city road (general case); (<b>e</b>) city road (construction area); (<b>f</b>) unpaved road.</p>
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<p>RLDA: (<b>a</b>) combination of country/highway/city/unpaved; (<b>b</b>) country road; (<b>c</b>) highway; (<b>d</b>) city road (general case); (<b>e</b>) city road (construction area); (<b>f</b>) unpaved road.</p>
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<p>RLDA: (<b>a</b>) combination of country/highway/city/unpaved; (<b>b</b>) country road; (<b>c</b>) highway; (<b>d</b>) city road (general case); (<b>e</b>) city road (construction area); (<b>f</b>) unpaved road.</p>
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<p>Damping force variation with respect to simulation mileage: (<b>a</b>) rebound hard; (<b>b</b>) rebound soft; (<b>c</b>) compression hard; (<b>d</b>) compression soft.</p>
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<p>Damper durability test facility.</p>
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<p>Damping force variation: (<b>a</b>) rebound hard; (<b>b</b>) rebound soft; (<b>c</b>) compression hard; (<b>d</b>) compression soft.</p>
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24 pages, 6563 KiB  
Article
A Variable Horizon Model Predictive Control for Magnetorheological Semi-Active Suspension with Air Springs
by Gang Li, Lin Zhong, Wenjun Sun, Shaohua Zhang, Qianjie Liu, Qingsheng Huang and Guoliang Hu
Sensors 2024, 24(21), 6926; https://doi.org/10.3390/s24216926 - 29 Oct 2024
Viewed by 526
Abstract
To improve the characteristics of traditional model predictive control (MPC) semi-active suspension that cannot achieve the optimal suspension control effect under different conditions, a variable horizon model predictive control (VHMPC) method is devised for magnetorheological semi-active suspension with air springs. Mathematical models are [...] Read more.
To improve the characteristics of traditional model predictive control (MPC) semi-active suspension that cannot achieve the optimal suspension control effect under different conditions, a variable horizon model predictive control (VHMPC) method is devised for magnetorheological semi-active suspension with air springs. Mathematical models are established for the magnetorheological dampers and air springs. Based on the improved hyperbolic tangent model, a forward model is established for the magnetorheological damper. The adaptive fuzzy neural network method is used to establish the inverse model of the magnetorheological damper. The relationship between different road excitation frequencies and the control effect of magnetorheological semi-active suspension with air springs is simulated, and the optimal prediction horizons under different conditions are obtained. The VHMPC method is designed to automatically switch the predictive horizon according to the road surface excitation frequency. The results demonstrate that under mixed conditions, compared with the traditional MPC, the VHMPC can improve the smoothness of the suspension by 2.614% and reduce the positive and negative peaks of the vertical vibration acceleration by 11.849% and 6.938%, respectively. Under variable speed road conditions, VHMPC improved the sprung mass acceleration, dynamic tire deformation, and suspension deflection by 7.191%, 7.936%, and 22.222%, respectively, compared to MPC. Full article
(This article belongs to the Section Environmental Sensing)
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<p>Mechanical performance test system of magnetorheological damper.</p>
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<p>Prototype of mechanical test system and magnetorheological damper.</p>
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<p>Mechanical characteristics of magnetorheological damper under different currents. (<b>a</b>) Damping force-displacement. (<b>b</b>) Damping force-velocity.</p>
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<p>The damping force under different frequencies and amplitudes. (<b>a</b>) Damping force-diplacement at different amplitudes. (<b>b</b>) Damping force-displacement at different frequencies.</p>
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<p>Particle swarm optimization algorithm identification process.</p>
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<p>Comparison between the simulation results of the forward model and the experimental data. (<b>a</b>) Damping force-displacement. (<b>b</b>) Damping force-velocity.</p>
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<p>ANFIS inverse model of magnetorheological damper.</p>
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<p>Training data validation of ANFIS inverse model (<b>a</b>) Tracking data. (<b>b</b>) Tracking error.</p>
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<p>Checking data validation of ANFIS inverse model. (<b>a</b>) Checking data. (<b>b</b>) Checking error.</p>
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<p>Working principle of air spring.</p>
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<p>Model of magnetorheological semi-active air suspension for 1/4 vehicle.</p>
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<p>Simulation results under different predictive horizon and road excitation frequencies.</p>
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<p>Simulation results under different predictive horizons and input roads.</p>
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<p>Control block diagram of VHMPC.</p>
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<p>Time domain comparison of VHMPC suspension performance indexes for mixed roadway. (<b>a</b>) Mixed road excitation input. (<b>b</b>) Sprung mass acceleration. (<b>c</b>) Dynamic tire deformation. (<b>d</b>) Suspension deflection.</p>
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<p>Time domain comparison of VHMPC suspension performance indexes for mixed roadway. (<b>a</b>) Mixed road excitation input. (<b>b</b>) Sprung mass acceleration. (<b>c</b>) Dynamic tire deformation. (<b>d</b>) Suspension deflection.</p>
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<p>Frequency domain comparison of VHMPC suspension performance indexes for mixed roadway. (<b>a</b>) Sprung mass acceleration (SMA). (<b>b</b>) Dynamic tire deformation (DTD). (<b>c</b>) Suspension deflection (SD). (<b>d</b>) Output damping force.</p>
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<p>Frequency domain comparison of VHMPC suspension performance indexes for mixed roadway. (<b>a</b>) Sprung mass acceleration (SMA). (<b>b</b>) Dynamic tire deformation (DTD). (<b>c</b>) Suspension deflection (SD). (<b>d</b>) Output damping force.</p>
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<p>Time domain comparison of VHMPC suspension performance indexes for variable speed pavement. (<b>a</b>) Variable speed pavement excitation input. (<b>b</b>) Sprung mass acceleration. (<b>c</b>) Dynamic tire deformation. (<b>d</b>) Suspension deflection.</p>
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<p>Frequency domain comparison of VHMPC suspension performance indexes for variable speed pavement. (<b>a</b>) Sprung mass acceleration (SMA). (<b>b</b>) Dynamic tire deformation (DTD). (<b>c</b>) Suspension deflection (SD). (<b>d</b>) Output damping force.</p>
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28 pages, 3447 KiB  
Article
Semi-Active Suspension Control Strategy Based on Negative Stiffness Characteristics
by Yanlin Chen, Shaoping Shen, Zhijie Li, Zikun Hu and Zhibin Li
Mathematics 2024, 12(21), 3346; https://doi.org/10.3390/math12213346 - 25 Oct 2024
Viewed by 570
Abstract
This paper investigates the potential of negative stiffness suspensions for enhanced vehicle vibration isolation. By analyzing and improving traditional control algorithms, we propose and experimentally validate novel skyhook, groundhook, and hybrid control strategies for suspensions with negative stiffness characteristics. We establish pavement models, [...] Read more.
This paper investigates the potential of negative stiffness suspensions for enhanced vehicle vibration isolation. By analyzing and improving traditional control algorithms, we propose and experimentally validate novel skyhook, groundhook, and hybrid control strategies for suspensions with negative stiffness characteristics. We establish pavement models, incorporate negative stiffness into suspension modeling, and develop a performance evaluation index. Our research identifies shortcomings of classical semi-active control algorithms and introduces a new band selector to combine improved control methods. Simulation results demonstrate that the proposed semi-active suspension control strategy based on negative stiffness effectively reduces body vibration and enhances vehicle ride performance. Full article
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<p>Control quarter suspension model.</p>
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<p>Ideal floor shed suspension model.</p>
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<p>Improved sky and groundhook negative stiffness semi-active suspension.</p>
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<p>Stiffness variation curve.</p>
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<p>Body and wheel vertical velocity relationship.</p>
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<p>Schematic diagram of body and wheel vertical velocity. It illustrates four distinct types of relative relationships (<b>a</b>–<b>d</b>).</p>
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<p>Spring load displacement comparison chart.</p>
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<p>Comparison of spring load speed.</p>
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<p>Comparison of spring load acceleration.</p>
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<p>Comparison of spring load speed.</p>
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<p>Comparison of spring load acceleration.</p>
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<p>Comparison of spring load displacement.</p>
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<p>Comparison of spring load speed.</p>
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<p>Comparison of spring load acceleration.</p>
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<p>Comparison of frequency response of body vibration amplitude.</p>
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<p>Comparison of unsprung load displacement of different control methods.</p>
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<p>Comparison of wheel dynamic deformation by different control methods.</p>
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<p>Comparison of spring load displacement.</p>
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<p>Comparison of spring load speed.</p>
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<p>Comparison of spring load acceleration.</p>
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<p>Comparison of frequency response of body vibration amplitude.</p>
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<p>Comparison of spring load displacement of different control methods. (<b>a</b>) Represents the original method, while (<b>b</b>) represents the negative stiffness method.</p>
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<p>Enlarge part. Figures (<b>b</b>–<b>d</b>) are magnified views of specific regions within (<b>a</b>). Figures (<b>f</b>–<b>h</b>) are magnified views of specific regions within (<b>e</b>).</p>
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<p>Enlarge part. Figures (<b>b</b>–<b>d</b>) are magnified views of specific regions within (<b>a</b>). Figures (<b>f</b>–<b>h</b>) are magnified views of specific regions within (<b>e</b>).</p>
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<p>Comparison of spring load speed of different control methods. (<b>a</b>) Represents the original method, while (<b>b</b>) represents the negative stiffness method.</p>
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<p>Comparison of different control methods for unsprung load speed. (<b>a</b>) Represents the original method, while (<b>b</b>) represents the negative stiffness method.</p>
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<p>Comparison of spring load acceleration of different control methods. (<b>a</b>) Represents the original method, while (<b>b</b>) represents the negative stiffness method.</p>
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<p>Comparison of unsprung acceleration of different control methods. (<b>a</b>) Represents the original method, while (<b>b</b>) represents the negative stiffness method.</p>
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<p>Comparison of suspension dynamic travel by different control methods. (<b>a</b>) Represents the original method, while (<b>b</b>) represents the negative stiffness method.</p>
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<p>Comparison of wheel dynamic deformation by different control methods.</p>
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<p>Comparison of spring load displacement of different control methods. (<b>a</b>) Represents the original method, while (<b>b</b>) represents the negative stiffness method.</p>
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<p>Comparison of unsprung load displacement of different control methods I.</p>
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<p>Comparison of spring load displacement of different control methods about local dynamic plot (<b>a</b>,<b>b</b>).</p>
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<p>Comparison of unsprung load displacement of different control methods II.</p>
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<p>Comparison of spring load displacement of different control methods about local dynamic plot (<b>a</b>,<b>b</b>).</p>
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<p>Comparison of frequency response of body vibration amplitude.</p>
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25 pages, 11565 KiB  
Article
Road-Adaptive Static Output Feedback Control of a Semi-Active Suspension System for Ride Comfort
by Donghyun Kim and Yonghwan Jeong
Actuators 2024, 13(10), 394; https://doi.org/10.3390/act13100394 - 3 Oct 2024
Viewed by 768
Abstract
This paper presents a static output feedback controller for a semi-active suspension system that provides improved ride comfort under various road roughness conditions. Previous studies on feedback control for semi-active suspension systems have primarily focused on rejecting low-frequency disturbances, such as bumps, because [...] Read more.
This paper presents a static output feedback controller for a semi-active suspension system that provides improved ride comfort under various road roughness conditions. Previous studies on feedback control for semi-active suspension systems have primarily focused on rejecting low-frequency disturbances, such as bumps, because the feedback controller is generally vulnerable to high-frequency disturbances, which can cause unintended large inputs. However, since most roads feature a mix of both low- and high-frequency disturbances, there is a need to develop a controller capable of responding effectively to both disturbances. In this work, road roughness is classified using the Burg method to select the optimal damping coefficient to respond to the high-frequency disturbance. The optimal control gain for the feedback controller is determined using the linear quadratic static output feedback (LQSOF) method, incorporating the optimal damping coefficient. The proposed algorithm was evaluated through simulations under bump scenarios with differing road roughness conditions. The simulation results demonstrated that the proposed algorithm significantly improved ride comfort compared to baseline algorithms under mixed disturbances. Full article
(This article belongs to the Section Actuators for Land Transport)
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<p>Overall architecture of the proposed semi-active suspension system, which is composed of the estimator and controller. CarSim with a variable damper map was used as a plant model for the simulation study.</p>
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<p>Block diagram of the suspension state estimator using high-pass filter (HPF) and low-pass filter (LPF) with vehicle geometry.</p>
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<p>Evaluation results of the stroke rate estimation.</p>
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<p>Data flow of the road roughness classifier based on the Burg method for real-time spectral analysis of the wheel acceleration.</p>
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<p>Results of road roughness classification on the condition of the continuous changes in road roughness: (<b>a</b>) vertical position of the wheel center, (<b>b</b>) vertical acceleration of the wheel center, and (<b>c</b>) estimated road roughness level.</p>
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<p>Half-car model for controller design.</p>
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<p>Process of the linear damping coefficient optimization for ride comfort.</p>
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<p>Conceptual diagram of the damper control strategy based on optimal damping control, shown by the green dotted line, and feedback control, shown as red and blue arrows, from linear damping.</p>
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<p>Configuration of the simulation study with the sensor model and CarSim vehicle model.</p>
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<p>Simulation results of the ideal case without road roughness: (<b>a</b>) vertical acceleration, (<b>b</b>) pitch angle, (<b>c</b>) pitch rate, (<b>d</b>) suspension stroke, and (<b>e</b>) suspension velocity.</p>
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<p>Simulation results of the ideal case without road roughness: (<b>a</b>) vertical acceleration, (<b>b</b>) pitch angle, (<b>c</b>) pitch rate, (<b>d</b>) suspension stroke, and (<b>e</b>) suspension velocity.</p>
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<p>Simulation results of road roughness class A: (<b>a</b>) vertical acceleration, (<b>b</b>) pitch angle, (<b>c</b>) pitch rate, (<b>d</b>) suspension stroke, and (<b>e</b>) suspension velocity.</p>
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<p>Simulation results of road roughness class A: (<b>a</b>) vertical acceleration, (<b>b</b>) pitch angle, (<b>c</b>) pitch rate, (<b>d</b>) suspension stroke, and (<b>e</b>) suspension velocity.</p>
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<p>Simulation results of road roughness class B: (<b>a</b>) vertical acceleration, (<b>b</b>) pitch angle, (<b>c</b>) pitch rate, (<b>d</b>) suspension stroke, and (<b>e</b>) suspension velocity.</p>
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<p>Simulation results of road roughness class B: (<b>a</b>) vertical acceleration, (<b>b</b>) pitch angle, (<b>c</b>) pitch rate, (<b>d</b>) suspension stroke, and (<b>e</b>) suspension velocity.</p>
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<p>Simulation results of road roughness class C: (<b>a</b>) vertical acceleration, (<b>b</b>) pitch angle, (<b>c</b>) pitch rate, (<b>d</b>) suspension stroke, and (<b>e</b>) suspension velocity.</p>
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<p>Simulation results of road roughness class C: (<b>a</b>) vertical acceleration, (<b>b</b>) pitch angle, (<b>c</b>) pitch rate, (<b>d</b>) suspension stroke, and (<b>e</b>) suspension velocity.</p>
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17 pages, 542 KiB  
Article
Optimal Control of a Semi-Active Suspension System Collaborated by an Active Aerodynamic Surface Based on a Quarter-Car Model
by Syed Babar Abbas and Iljoong Youn
Electronics 2024, 13(19), 3884; https://doi.org/10.3390/electronics13193884 - 30 Sep 2024
Viewed by 759
Abstract
This paper addresses the trade-off between ride comfort and road-holding capability of a quarter-car semi-active suspension system, collaborated by an active aerodynamic surface (AAS), using an optimal control policy. The semi-active suspension system is more practical to implement due to its low energy [...] Read more.
This paper addresses the trade-off between ride comfort and road-holding capability of a quarter-car semi-active suspension system, collaborated by an active aerodynamic surface (AAS), using an optimal control policy. The semi-active suspension system is more practical to implement due to its low energy consumption than the active suspension system while significantly improving ride comfort. First, a model of the two-DOF quarter-car semi-active suspension in the presence of an active airfoil with two weighting sets based on ride comfort and road-holding preferences is presented. Then, a comprehensive comparative study of the improved target performance indices with various suspension systems is performed to evaluate the proposed suspension performance. Time-domain and frequency-domain analyses are conducted in MATLAB® (R2024a). From the time-domain analysis, the total performance measure is enhanced by about 50% and 35 to 45%, respectively, compared to passive and active suspension systems. The results demonstrate that a semi-active suspension system with an active aerodynamic control surface simultaneously improves the conflicting target parameters of passenger comfort and road holding. Utilizing the aerodynamic effect, the proposed system enhances the vehicle’s dynamic stability and passenger comfort compared to other suspension systems. Full article
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<p>Simplified models of the 2-DOF quarter of the vehicle: (<b>a</b>) ASS with an active airfoil [<a href="#B25-electronics-13-03884" class="html-bibr">25</a>], (<b>b</b>) SASS with an active airfoil, (<b>c</b>) SASS only [<a href="#B26-electronics-13-03884" class="html-bibr">26</a>].</p>
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<p>Block diagram for an SASS with an active airfoil.</p>
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<p>Car body acceleration for various suspension systems.</p>
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<p>Car suspension deflection for various suspension systems.</p>
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<p>Car tire deflection for various suspension systems.</p>
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<p>Road shock against various systems.</p>
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<p>Sprung mass acceleration for various suspension systems.</p>
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<p>Suspension deflection for various suspension systems.</p>
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<p>Dynamic tire deflection for various suspension systems.</p>
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23 pages, 7874 KiB  
Review
Advancements in Semi-Active Automotive Suspension Systems with Magnetorheological Dampers: A Review
by Zunming Wang, Chi Liu, Xu Zheng, Liang Zhao and Yi Qiu
Appl. Sci. 2024, 14(17), 7866; https://doi.org/10.3390/app14177866 - 4 Sep 2024
Cited by 2 | Viewed by 2442
Abstract
Magnetorheological (MR) dampers have significantly advanced automotive suspension systems by providing adaptable damping characteristics in response to varying road conditions and driving dynamics. This review offers a comprehensive analysis of the evolution and integration of MR dampers in semi-active suspension systems. Semi-active systems [...] Read more.
Magnetorheological (MR) dampers have significantly advanced automotive suspension systems by providing adaptable damping characteristics in response to varying road conditions and driving dynamics. This review offers a comprehensive analysis of the evolution and integration of MR dampers in semi-active suspension systems. Semi-active systems present an optimal balance by integrating the simplicity inherent in passive systems with the adaptability characteristic of active systems, while mitigating the substantial energy consumption. The fundamental principles of MR technology, the design of MR dampers, and the diverse control strategies employed to optimize suspension performance were examined. The classical, modern, and intelligent control methods, along with the related research, were emphasized. Based on the above-mentioned methods, the benefits of MR semi-active control were highlighted, while the challenges and future research directions in MR damper technology were also addressed. Through a synthesis of recent research findings and practical applications, this paper underscores the advancements in MR-based semi-active suspension systems and their promising prospects in the automotive industry. Full article
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<p>The structure of a typical suspension system.</p>
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<p>Types of vehicle suspensions: (<b>a</b>) passive suspensions; (<b>b</b>) semi-active suspensions; (<b>c</b>) active suspensions.</p>
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<p>The MRD working principle [<a href="#B25-applsci-14-07866" class="html-bibr">25</a>].</p>
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<p>Configuration of a twin-tube MRD integrated into the semi-active suspension system [<a href="#B16-applsci-14-07866" class="html-bibr">16</a>].</p>
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<p>Schematics of a double adjustable shock absorber: (<b>a</b>) cross section of the pneumatic reservoir that contains the compression adjuster; (<b>b</b>) cross section of the main hydraulic cylinder that contains the piston head assembly.</p>
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<p>The illustration of the design of three-coil mixed mode MRD [<a href="#B6-applsci-14-07866" class="html-bibr">6</a>].</p>
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<p>Structure of the MRD with a piston head featuring bypass holes [<a href="#B36-applsci-14-07866" class="html-bibr">36</a>].</p>
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<p>The proposed enhanced energy-efficient skyhook control [<a href="#B40-applsci-14-07866" class="html-bibr">40</a>]: (<b>a</b>) quarter-car model of a semi-active suspension system; (<b>b</b>) block diagram of the control system.</p>
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<p>Schematic diagram of the vehicle semi-active MR suspension system with controller proposed in [<a href="#B41-applsci-14-07866" class="html-bibr">41</a>].</p>
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<p>Schematic diagram of the LQR controller proposed in [<a href="#B57-applsci-14-07866" class="html-bibr">57</a>].</p>
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<p>A preview controller of semi-active suspension based on a half-car model proposed in [<a href="#B63-applsci-14-07866" class="html-bibr">63</a>]: (<b>a</b>) the control-oriented semi-active half-car model; (<b>b</b>) the preview control scheme.</p>
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<p>Schematic diagram of the hardware-in-the-loop-simulation (HILS) for the full-vehicle MR suspension system proposed in [<a href="#B16-applsci-14-07866" class="html-bibr">16</a>].</p>
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<p>Schematic diagram of the adaptive sliding mode fault-tolerant control scheme.</p>
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<p>FSMC structure diagram of MR semi-active air suspension proposed in [<a href="#B93-applsci-14-07866" class="html-bibr">93</a>].</p>
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<p>Structure diagram of the adaptive neural network controller for vehicle suspension proposed in [<a href="#B20-applsci-14-07866" class="html-bibr">20</a>].</p>
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<p>The implementation of MRD in production vehicles.</p>
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26 pages, 3728 KiB  
Article
Experimental Validation of Truck Cab Suspension Model and Ride Comfort Improvement under Various Semi-Active Control Strategies
by Qihao Sun, Changcheng Yin and Baohua Wang
Processes 2024, 12(9), 1880; https://doi.org/10.3390/pr12091880 - 2 Sep 2024
Viewed by 933
Abstract
The semi-active cab suspension system for trucks is gaining increasing importance due to its economic advantages, low energy consumption, and significant enhancement of ride comfort. This paper investigates the effects of three control methods on improving ride comfort of semi-active cab suspension systems [...] Read more.
The semi-active cab suspension system for trucks is gaining increasing importance due to its economic advantages, low energy consumption, and significant enhancement of ride comfort. This paper investigates the effects of three control methods on improving ride comfort of semi-active cab suspension systems under random and bump road conditions: Proportional-Integral-Derivative (PID) control, fuzzy PID control, and Model Predictive Control (MPC). Initially, an accurate multi-degree-of-freedom truck cab suspension model was developed and validated through actual road tests. Based on this model, three control strategies were designed and implemented. Finally, the effectiveness of each control strategy was evaluated under various road conditions, including random and bump road scenarios. The results indicate that these control strategies can effectively reduce vibrations and impacts, significantly improving ride comfort. This improvement is crucial for alleviating driver fatigue and enhancing driving safety. Among them, the MPC control showed superior performance, reducing vibrations by at least 31% under both random and bump road conditions, outperforming PID and Fuzzy PID in terms of effectiveness and robustness. Full article
(This article belongs to the Section Automation Control Systems)
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<p>Schematic diagram of the truck cab suspension model.</p>
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<p>The Class A road.</p>
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<p>The spatial power spectral density of the Class A road.</p>
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<p>The three-meter road inspection ruler.</p>
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<p>Experimental equipment: (<b>a</b>) Dongfeng Tianjin experimental truck. (<b>b</b>) LMS data acquisition equipment. (<b>c</b>) Triaxial accelerometer.</p>
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<p>Sensor installation position.</p>
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<p>Experimental data filtering.</p>
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<p>Model validation methods.</p>
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<p>Simulation Data Comparison with Actual Measurement Data at a Speed of 20 km/h on Grade A Road Surface.</p>
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<p>Simulation Data Comparison with Actual Measurement Data at a Speed of 40 km/h on Grade A Road Surface.</p>
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<p>Simulation Data Comparison with Actual Measurement Data at a Speed of 60 km/h on Grade A Road Surface.</p>
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<p>Analysis of vertical, roll, and pitch vibration of the cab.</p>
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<p>Performance Comparison of Three Control Strategies on Grade A Road Surface: (<b>a</b>) Vertical Acceleration. (<b>b</b>) Pitch Angle Acceleration. (<b>c</b>) Roll Angle Acceleration.</p>
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<p>Performance Comparison of Three Control Strategies on Grade B Road Surface: (<b>a</b>) Vertical Acceleration. (<b>b</b>) Pitch Angle Acceleration. (<b>c</b>) Roll Angle Acceleration.</p>
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<p>Comparison of Three Control Methods on Bumpy Road: (<b>a</b>) Vertical Acceleration. (<b>b</b>) Pitch Angle Acceleration. (<b>c</b>) Roll Angle Acceleration.</p>
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<p>Fitted Curve of the Front Suspension Damper in the Cab.</p>
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<p>Fitted Curve of the Rear Suspension Damper in the Cab.</p>
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<p>Fitted Curve of the Front Suspension.</p>
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<p>The Relationship Between <math display="inline"><semantics> <msub> <mi>k</mi> <mn>1</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>k</mi> <mn>2</mn> </msub> </semantics></math> Values and Changes in Tire Inflation Pressure: (<b>a</b>) The Relationship Between the <math display="inline"><semantics> <msub> <mi>k</mi> <mn>1</mn> </msub> </semantics></math> Value and Changes in Tire Inflation Pressure. (<b>b</b>) The Relationship Between the <math display="inline"><semantics> <msub> <mi>k</mi> <mn>2</mn> </msub> </semantics></math> Value and Changes in Tire Inflation Pressure.</p>
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20 pages, 6889 KiB  
Article
Sliding Mode Control for Semi-Active Suspension System Based on Enhanced African Vultures Optimization Algorithm
by Yuyi Li, Zhe Fang, Kai Zhu and Wangshui Yu
World Electr. Veh. J. 2024, 15(8), 380; https://doi.org/10.3390/wevj15080380 - 21 Aug 2024
Viewed by 896
Abstract
To improve the ride comfort and driving stability of automobiles, an optimal sliding mode control (OSMC) strategy based on the enhanced African vultures optimization algorithm (EAVOA) is proposed. Firstly, the structure and operating principle of a semi-active suspension system (SASS) with a magnetorheological [...] Read more.
To improve the ride comfort and driving stability of automobiles, an optimal sliding mode control (OSMC) strategy based on the enhanced African vultures optimization algorithm (EAVOA) is proposed. Firstly, the structure and operating principle of a semi-active suspension system (SASS) with a magnetorheological damper (MRD) is comprehensively introduced. Secondly, the OSMC is designed based on a quarter-vehicle suspension model with two degrees of freedom (DOF) to meet the Hurwitz stability theory. Simultaneously, the EAVOA is employed to optimize the control coefficients of the sliding mode surface and the control law parameters. Finally, the EAVOA-OSMC control strategy is utilized to construct the simulation model in MATLAB/Simulink (R2018b), providing a comprehensive analysis for passive suspension systems (PSSs) and suspensions with SMC control. The simulation results demonstrate that the EAVOA-OSMC control strategy outperforms SMC controllers, offering a better control performance in real application. Full article
(This article belongs to the Special Issue Design Theory, Method and Control of Intelligent and Safe Vehicles)
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<p>The quarter automotive model with 2 DOF.</p>
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<p>The hyperbolic simulation model.</p>
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<p>The relationship between time and damping force.</p>
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<p>The structure for the EAVOA.</p>
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<p>The vulture particle swarm distribution chart: (<b>a</b>) the initial distribution of the vulture population; (<b>b</b>) the distribution of the vulture population when <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>; (<b>c</b>) the distribution of the vulture population when <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>; and (<b>d</b>) the distribution of the vulture population when <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mn>3.9</mn> </mrow> </semantics></math>.</p>
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<p>The results for <math display="inline"><semantics> <mrow> <msub> <mi>α</mi> <mi>c</mi> </msub> </mrow> </semantics></math> with iteration numbers.</p>
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<p>(<b>a</b>) The perturbation function; and (<b>b</b>) the hunger function.</p>
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<p>Operation diagram of vultures in a full-stomach state.</p>
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<p>The simulation structure flow.</p>
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<p>The simulation structure diagram.</p>
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<p>Control performance comparison on B-grade road: (<b>a</b>) the B-grade random road (reprinted from Ref. [<a href="#B23-wevj-15-00380" class="html-bibr">23</a>]); (<b>b</b>) <span class="html-italic">VAA</span>; (<b>c</b>) <span class="html-italic">SDD</span>; and (<b>d</b>) <span class="html-italic">WDD</span>.</p>
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<p>Control performance comparison on D-grade road: (<b>a</b>) the D-grade road; (<b>b</b>) <span class="html-italic">VAA</span>; (<b>c</b>) <span class="html-italic">SDD</span>; and (<b>d</b>) <span class="html-italic">WDD</span>.</p>
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<p>(<b>a</b>) The comprehensive evaluation chart; and (<b>b</b>) the damping force.</p>
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39 pages, 7497 KiB  
Review
Review of Fault-Tolerant Control Methods for Suspension Systems: From Road Vehicles to Maglev Trains
by Fei Ni, Yifan Luo, Junqi Xu, Dachuan Liu, Yougang Sun and Wen Ji
Mathematics 2024, 12(16), 2576; https://doi.org/10.3390/math12162576 - 20 Aug 2024
Viewed by 1141
Abstract
Road vehicles and maglev trains have garnered significant attention, with their suspension systems being crucial for safe and stable performance. However, these systems can be compromised by faults such as sensor and actuator failures, posing risks to stability and safety. This review explores [...] Read more.
Road vehicles and maglev trains have garnered significant attention, with their suspension systems being crucial for safe and stable performance. However, these systems can be compromised by faults such as sensor and actuator failures, posing risks to stability and safety. This review explores fault-tolerant controls for suspension systems, driven by the need to enhance fault tolerance in such scenarios. We examine the dynamic similarities between the semi-active/active suspension systems in road vehicles and the suspension systems in maglev trains, offering a comprehensive summary of fault-tolerant control strategies for both. Our analysis covers the histories, technical characteristics, fundamentals, modeling, mathematical derivations, and control objectives of both systems. The review categorizes fault-tolerant control methods into hardware redundancy, passive fault-tolerant control, and active fault-tolerant control. We evaluate the advantages and disadvantages of these strategies and propose future directions for the development of fault-tolerant control in suspension systems. Full article
(This article belongs to the Section Computational and Applied Mathematics)
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<p>General strategy for active fault-tolerant control.</p>
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<p>General strategy for passive fault-tolerant control.</p>
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<p>A fault-tolerant control method for aircraft.</p>
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<p>A fault-tolerant control method for road vehicles.</p>
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<p>A fault-tolerant control method for a magnetic suspension system (modified from [<a href="#B27-mathematics-12-02576" class="html-bibr">27</a>]).</p>
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<p>Categorization charts of past studies.</p>
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<p>Four-degree-of-freedom model of a semi-vehicle.</p>
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<p>Two-degree-of-freedom model of a 1/4 vehicle (modified from [<a href="#B47-mathematics-12-02576" class="html-bibr">47</a>]).</p>
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<p>Schematic diagram of EMS-type levitation control module; (<b>a</b>) low- and medium-speed maglev train; and (<b>b</b>) high-speed maglev train.</p>
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<p>Illustration of EMS-type suspension system.</p>
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<p>Schematic diagram of the mathematical model of the vehicle-guideway interaction system considering a secondary suspension.</p>
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<p>Structure of overlapping model.</p>
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<p>Dynamic event-triggered fault-tolerant control of vehicle active suspension systems (modified from [<a href="#B66-mathematics-12-02576" class="html-bibr">66</a>]).</p>
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<p>A fault-tolerant controller for SMC based on RBF (modified from [<a href="#B70-mathematics-12-02576" class="html-bibr">70</a>]).</p>
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<p>A fault-tolerant control method based on a non-singular fast terminal sliding mode controller (modified from [<a href="#B72-mathematics-12-02576" class="html-bibr">72</a>]).</p>
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<p>An active fault-tolerant control of active suspension based on reconfiguration (modified from [<a href="#B82-mathematics-12-02576" class="html-bibr">82</a>]).</p>
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<p>An active fault-tolerant control method based on fault compensation (modified from [<a href="#B89-mathematics-12-02576" class="html-bibr">89</a>]).</p>
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<p>Working principle of dual hot standby (modified from [<a href="#B100-mathematics-12-02576" class="html-bibr">100</a>]).</p>
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<p>A fault detection scheme based on iFD (modified from [<a href="#B118-mathematics-12-02576" class="html-bibr">118</a>]).</p>
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<p>Observer-based fault diagnosis and fault localization (modified from [<a href="#B120-mathematics-12-02576" class="html-bibr">120</a>]).</p>
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<p>A reconfiguration-based fault-tolerant control approach (modified from [<a href="#B132-mathematics-12-02576" class="html-bibr">132</a>]).</p>
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<p>Schematic diagram of a switching-based active fault-tolerant control under sensor failure.</p>
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<p>Active fault-tolerant control strategy based on switching policy (modified from [<a href="#B138-mathematics-12-02576" class="html-bibr">138</a>]).</p>
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<p>An online optimized active fault-tolerant control strategy (modified from [<a href="#B148-mathematics-12-02576" class="html-bibr">148</a>]).</p>
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