Search Results (428)

In this paper, the problem of preview optimal control for second-order nonlinear systems for marine vessels is discussed on a fully actuated dynamic model. First, starting from a kinematic and dynamic model of a three-degrees-of-freedom (DOF) marine vessel, we derive a fully actuated second-order dynamic model that involves only the ship’s position and yaw angle. Subsequently, through the higher-order systems methodology, the nonlinear terms in the system were eliminated, transforming the system into a one-order parameterized linear system. Next, we designed an internal model compensator for the reference signal and constructed a new augmented error system based on this compensator. Then, using optimal control theory, we designed the optimal preview controller for the parameterized linear system and the corresponding feedback parameter matrices, which led to the preview controller for the original second-order nonlinear system. Finally, a numerical simulation indicates that the controller designed in this paper is highly effective. Full article

This paper proposes the fixed-time prescribed performance optimal consensus control method for stochastic nonlinear multi-agent systems with sensor faults. The consensus error converges to the prescribed performance bounds in fixed-time by an improved performance function and coordinate transformation. Due to the unknown faults in sensors, the system states cannot be gained correctly; therefore, an adaptive compensation strategy is constructed based on the approximation capabilities of neural networks to solve the negative impact of sensor failures. The reinforcement-learning-based backstepping method is proposed to realize the optimal control of the system. Utilizing Lyapunov stability theory, it is shown that the designed controller enables the consensus error to converge to the prescribed performance bounds in fixed time and that all signals in the closed-loop system are bounded in probability. Finally, the simulation results prove the effectiveness of the proposed method. Full article

In this work, we implemented a short-reach real-time optical communication system using MLP for pre-distortion. Lookup table (LUT) algorithms are commonly employed for pre-distortion in intensity modulation and direct detection (IM/DD) systems. However, storage limitations typically restrict the LUT pattern length to 9, limiting its effectiveness in compensating for nonlinear effects. A multilayer perceptron (MLP) can overcome this limitation by predicting errors and generating pre-distorted signals, thus replacing the extensive storage requirements of LUTs with minimal computational resources. The MLP-based digital pre-distortion (MLP-DPD) technique enables the creation of long-pattern LUTs for improved nonlinear compensation. In this work, an MLP-DPD scheme was implemented on a field-programmable gate array (FPGA). The FPGA was used to generate a 14.7456 GBaud pre-distorted pulse amplitude modulation 4-level (PAM4) signal. This signal was then transmitted over 20 km of standard single-mode fiber (SSMF). At the receiver, the parallel constant modulus algorithm (PCMA) was applied for signal processing. The bit error rate (BER) achieved met the 2.4 × 10⁻² threshold for soft-decision forward error correction (SD-FEC), enabling a net transmission bit rate of 24.576 Gbit/s. This approach demonstrates the feasibility of using MLP-DPD for effective nonlinear compensation in high-speed optical communication systems with limited resources. Full article

Over the last ten years, the need for high-resolution time-domain digital signal production has grown exponentially. More than ever, applications call for a digital-to-time converter (DTC) that is extremely accurate and precise. Skew compensation and camera shutter operation represent just a few examples of such applications. The advantages of adopting a flexible and rapid time-to-market strategy focused on fast prototyping using programmable logic devices—such as field-programmable gate arrays (FPGAs) and system-on-chip (SoC)—have become increasingly evident. These benefits outweigh those of performance-focused yet expensive application-specific integrated circuits (ASICs). Despite the availability of various architectures, the high non-recurring engineering (NRE) costs make them unsuitable for low-volume production, especially in research or prototyping environments. To address this trend, we introduce an innovative DTC IP-Core with a resolution, also known as least significant bit (LSB), of 52 ps, compatible with all Xilinx 7-Series FPGAs and SoCs. Measurements have been performed on a low-end Artix-7 XC7A100TFTG256-2, guaranteeing a jitter lower than 50 ps r.m.s. and offering a high dynamic range up to 56 ms. With resource utilization below 1% and a dynamic power dissipation of 285 mW for our target FPGA, the design maintains excellent differential and integral nonlinearity errors (DNL/INL) of 1.19 LSB and 1.56 LSB, respectively. Full article

This paper presents a robust control strategy for trajectory-tracking control of Skid-Steer Mobile Manipulators (SSMMs) using a Robust Nonlinear Model Predictive Control (R-NMPC) approach that minimises trajectory-tracking errors while overcoming model uncertainties and terra-mechanical disturbances. The proposed strategy is aimed at counteracting the effects of disturbances caused by the slip phenomena through the wheel–terrain contact and bidirectional interactions propagated by mechanical coupling between the SSMM base and arm. These interactions are modelled using a coupled nonlinear dynamic framework that integrates bounded uncertainties for the mobile base and arm joints. The model is developed based on principles of full-body energy balance and link torques. Then, a centralized control architecture integrates a nominal NMPC (disturbance-free) and ancillary controller based on Active Disturbance-Rejection Control (ADRC) to strengthen control robustness, operating the full system dynamics as a single robotic body. While the NMPC strategy is responsible for the trajectory-tracking control task, the ADRC leverages an Extended State Observer (ESO) to quantify the impact of external disturbances. Then, the ADRC is devoted to compensating for external disturbances and uncertainties stemming from the model mismatch between the nominal representation and the actual system response. Simulation and field experiments conducted on an assembled Pioneer 3P-AT base and Katana 6M180 robotic arm under terrain constraints demonstrate the effectiveness of the proposed method. Compared to non-robust controllers, the R-NMPC approach significantly reduced trajectory-tracking errors by 79.5% for mobile bases and 42.3% for robot arms. These results highlight the potential to enhance robust performance and resource efficiency in complex navigation conditions. Full article

An Image-Based Visual Servoing Control (IBVS) structure for target tracking by Unmanned Aerial Vehicles (UAVs) is presented. The scheme contains two stages. The first one is a sliding-model controller (SMC) that allows one to track a target with a UAV; the control strategy is designed in the function of the image. The proposed SMC control strategy is commonly used in control systems that present high non-linearities and that are always exposed to external disturbances; these disturbances can be caused by environmental conditions or induced by the estimation of the position and/or velocity of the target to be tracked. In the second instance, a controller is placed to compensate the UAV dynamics; this is a controller that allows one to compensate the velocity errors that are produced by the dynamic effects of the UAV. In addition, the corresponding stability analysis of the sliding mode-based visual servo controller and the sliding mode dynamic compensation control is presented. The proposed control scheme employs the kinematics and dynamics of the robot by presenting a cascade control based on the same control strategy. In order to evaluate the proposed scheme for tracking moving targets, experimental tests are carried out in a semi-structured working environment with the hexarotor-type aerial robot. For detection and image processing, the Opencv C++ library is used; the data are published in an ROS topic at a frequency of 50 Hz. The robot controller is implemented in the mathematical software Matlab. Full article

This article concerns the issue of adaptive fuzzy command-filtered fixed-time control in the context of a category of nonlinear systems characterized by mismatched disturbances and unknown nonlinear functions. The backstepping-based disturbance observers are created to alleviate the effects of mismatched disturbances and Fuzzy logic systems are brought into play to model those terms that are unknown. To address the complexity explosion issue in traditional backstepping control, this paper utilizes fixed-time command filters (FTCFs) to design a novel control approach. Moreover, filtering error compensation mechanisms are developed to eliminate the errors introduced by the FTCFs. This paper derives a novel adaptive fixed-time control protocol that successfully conquers the difficulties posed by unknown nonlinear functions and mismatched disturbances. The protocol, implemented within a backstepping framework, guarantees the boundedness of all signals and tracking errors within fixed time. The efficacy of the derived control protocol is illustrated through simulation examples. Full article

Although integrated joint torque sensors in robots dispel the need for external force/torque sensors at the wrist to measure interactions, an inherent challenge is that they also measure the robot’s intrinsic dynamics. This is especially problematic for delicate robot manipulation tasks, where interaction forces may be comparable to the robot intrinsic dynamics. Therefore, the intrinsic dynamics must first be experimentally estimated under no-load conditions, when the measurement only consists of torques due to the transmission of the robot actuator, before external interactions may be measured. In this work, we propose an approach for identifying and predicting the intrinsic dynamics using linear regression with non-linear radial basis functions. Then, we validate this regression on a wheel-bearing turning task, in which its friction is a measure of quality, and thus must be accurately measured. The results showed that the bearing torque measured by the joint 7 torque sensor was within an RMS error of 11% of the torque measured by the external force/torque sensor. This error is much lower than that before our proposed model in compensating the intrinsic dynamics of the robot arm. Full article

This paper presents the constitution of a computationally intelligent self-adaptive steering controller for a lawn-mowing robot to yield robust trajectory tracking and disturbance rejection behavior. The conventional fixed-gain proportional–integral–derivative (PID) control procedure lacks the flexibility to deal with the environmental indeterminacies, coupling issues, and intrinsic nonlinear dynamics associated with the aforementioned nonholonomic system. Hence, this article contributes to formulating a self-adaptive single-neuron PID control system that is driven by an extended Kalman filter (EKF) to ensure efficient learning and faster convergence speeds. The neural adaptive PID control formulation improves the controller’s design flexibility, which allows it to effectively attenuate the tracking errors and improve the system’s trajectory tracking accuracy. To supplement the controller’s robustness to exogenous disturbances, the adaptive PID control signal is modulated with an auxiliary fuzzy-immune system. The fuzzy-immune system imitates the automatic self-learning and self-tuning characteristics of the biological immune system to suppress bounded disturbances and parametric variations. The propositions above are verified by performing the tailored hardware in the loop experiments on a differentially driven lawn-mowing robot. The results of these experiments confirm the enhanced trajectory tracking precision and disturbance compensation ability of the prescribed control method. Full article

A flight controller formulation based on incremental nonlinear dynamics inversion (INDI) control with nonlinear disturbance observer (NDO) is proposed. INDI control is a nonlinear controller based on incremental dynamics. Aimed to attain robustness for nonlinear dynamics inversion (NDI)-based controller, incremental dynamics are derived using the first-order Talyor series expansion to nonlinear systems. The incremental dynamics-based controller requires information on state derivative terms to strengthen the robustness property of the nonlinear controller. The proposed controller utilizes the first-order low-pass filter to obtain the state derivative estimate to implement incremental dynamics into the system. Because the incremental form creates uncertainty term which is an aftermath of the Taylor series expansion, the proposed controller adopts the NDO to eliminate this effect. The controller is applied to the generic transport model which was developed by NASA for simulation purposes. The proposed NDO-based INDI control underwent simulations, together with an INDI controller without disturbance observer, and showed that the developed method results in better performances, providing important advantages where it compensates the uncertainties, removes the steady-state error, and shows less oscillating longitudinal body rate response than the baseline controller, desirable for aerodynamics applications with faster system response. Full article

This study focuses on microgrid systems incorporating hybrid renewable energy sources (HRESs) with battery energy storage (BES), both essential for ensuring reliable and consistent operation in off-grid standalone systems. The proposed system includes solar energy, a wind energy source with a synchronous turbine, and BES. Hybrid particle swarm optimizer (PSO) and a genetic algorithm (GA) combined with active disturbance rejection control (ADRC) (PSO-GA-ADRC) are developed to regulate both the frequency and amplitude of the AC bus voltage via a load-side converter (LSC) under various operating conditions. This approach further enables efficient management of accessible generation and general consumption through a bidirectional battery-side converter (BSC). Additionally, the proposed method also enhances power quality across the AC link via mentoring the photovoltaic (PV) inverter to function as shunt active power filter (SAPF), providing the desired harmonic-current element to nonlinear local loads as well. Equipped with an extended state observer (ESO), the hybrid PSO-GA-ADRC provides efficient estimation of and compensation for disturbances such as modeling errors and parameter fluctuations, providing a stable control solution for interior voltage and current control loops. The positive results from hardware-in-the-loop (HIL) experimental results confirm the effectiveness and robustness of this control strategy in maintaining stable voltage and current in real-world scenarios. Full article

This study investigates the problem of tracking the trajectory of a dynamic positioning (DP) ship under sudden surges of elevated sea states. First, the tracking problem is reformulated as an error calibration problem through the introduction of fully actuated system (FAS) approaches, thereby simplifying controller design. Second, a predefined-time control term is designed to maintain the convergence time of the trajectory tracking error within a specified range; however, the upper bound of the perturbation must be estimated in advance. The high sea state during operation can result in an abrupt change in the upper bound of disturbance, thereby affecting the control accuracy and stability of the system. Therefore, a linear control matrix is developed to eliminate the system’s dependence on the estimation of the upper bound of disturbance following smooth switching, thereby achieving control decoupling and providing a conservative switching time. Additionally, a nonlinear reduced-order expansion observer (RESO) is constructed for feedforward compensation. The stability of the system is demonstrated using the Lyapunov function, indicating that the selection of appropriate poles can theoretically enhance the system’s convergence with greater control accuracy and robustness after switching. Finally, the effectiveness of the proposed method is validated through simulations and comparative experiments. Full article

This paper investigates the application of fractional proportional–resonant controllers within the voltage control loop of grid-forming inverters. The use of such controllers introduces an additional degree of freedom, enabling greater flexibility in manipulating frequency trajectories. This flexibility can be harnessed to improve tracking error and enhance disturbance rejection, particularly in applications requiring precise voltage regulation. The paper conducts a conceptual stability analysis of ideal fractional proportional–resonant controllers using the Nyquist criterion. A tuning procedure based on robustness criteria for the proposed controller is also addressed. This tuning strategy is used to compare different controllers under the same conditions. In addition, a sensitivity analysis is provided, comparing the performance of fractional proportional–resonant controllers with traditional proportional–resonant controllers equipped with harmonic compensation. The controller’s formulation and performance are validated through simulations and tested with a 20 kVA inverter under high non-linear loads. Compared to classical control approaches, the fractional tuning parameter enhances tracking performance, reduces phase delay, and improves disturbance rejection. These improvements are achieved with a controller designed to minimise computational demands in terms of memory usage and execution time. Full article

The parallel driving soft manipulator with multiple extensors and contractile pneumatic artificial muscles (PAMs) is able to operate continuously and has varying stiffness, achieving smooth movements and a fundamental trade-off between flexibility and stiffness. Owing to the hysteresis of PAMs and actuator couplings, the manipulator outputs display coupled hysteresis behaviors with stiffness dependence, causing significant positioning errors. For precise positioning control, this paper takes the lead in proposing a comprehensive model aimed at accurately predicting the coupled hysteresis behavior with the stiffness dependence of the soft manipulator. The model consists of an inherent hysteresis submodule, an actuator coupling submodule, and a stiffness-dependent submodule in series. The asymmetrical hysteresis nonlinearity of the PAM is established by the generalized Prandtl–Ishlinskii model in the inherent hysteresis submodule. The serial actuator coupling submodule is dedicated to modeling the actuator couplings, and the stiffness-dependent submodule is implemented with a fuzzy neural network to characterize the stiffness dependence and other system nonlinearities. In addition, an inverse compensator on the basis of the proposed model is conducted. Experiments demonstrate that this model possesses high accuracy and good generalization, and its compensator is effective in decoupling and mitigating hysteresis coupling of the manipulator. The proposed model and control methods significantly improve the positioning accuracy of the pneumatic soft manipulator. Full article

This paper presents a control strategy for a 4Y octocopter aircraft that is influenced by multiple actuator faults and external disturbances. The approach relies on a disturbance observer, adaptive type-2 fuzzy sliding mode control scheme, and type-1 fuzzy inference system. The proposed control approach is distinct from other tactics for controlling unmanned aerial vehicles because it can simultaneously compensate for actuator faults and external disturbances. The suggested control technique incorporates adaptive control parameters in both continuous and discontinuous control components. This enables the production of appropriate control signals to manage actuator faults and parametric uncertainties without relying only on the robust discontinuous control approach of sliding mode control. Additionally, a type-1 fuzzy logic system is used to build a fuzzy hitting control law to eliminate the occurrence of chattering phenomena on the integral sliding mode control. In addition, in order to keep the discontinuous control gain in sliding mode control at a small value, a nonlinear disturbance observer is constructed and integrated to mitigate the influence of external disturbances. Moreover, stability analysis of the proposed control method using Lyapunov theory showcases its potential to uphold system tracking performance and minimize tracking errors under specified conditions. The simulation results demonstrate that the proposed control strategy can significantly reduce the chattering effect and provide accurate trajectory tracking in the presence of actuator faults. Furthermore, the efficacy of the recommended control strategy is shown by comparative simulation results of 4Y octocopter under different failing and uncertain settings. Full article