Search Results (13)

Autonomous control of lunar landers is essential for successful space missions, where precision and efficiency are crucial. This study presents a novel control strategy that leverages proportional, integral, and derivative (PID) controllers to manage the altitude, attitude, and position of a lunar lander, considering time-varying mass and sloshing behavior. Additionally, neural network models are developed, to approximate the lander’s mass properties as they change during descent. The challenge lies in the significant mass variations due to fuel, oxidizer, and pressurant consumption, which affect the lander’s inertia and sloshing behavior and complicate control efforts. We have developed a control-oriented model incorporating these mass dynamics, employing multiple PID controllers to linearize the system and enhance control precision. Altitude is maintained by one PID controller, while two others adjust the thrust vector control (TVC) gimbal angles to manage pitch and roll, with a fourth controller governing yaw via a reaction control system (RCS). A cascade PD controller further manages position by feeding commands to the attitude controllers, ensuring the lander reaches its target location. The lander’s TVC mechanism, equipped with a spherical gimbal, provides thrust in the desired direction, with control angles $\alpha$ and $\beta$ regulated by the PID controllers. To improve the model’s accuracy, we have introduced time delays caused by fluid dynamics and actuator response, modeled via computational fluid dynamics (CFD). Fluid sloshing effects are also simulated as external forces acting on the lander. The neural networks are trained using data derived from computer-aided design (CAD) simulations of the lander vehicle, specifically the inertia tensor and the center of mass (COM) based on the varying mass levels in the tanks. The trained neural networks (NNs) can then use lander tank levels and orientation to inform and accurately predict the lander’s COM and inertia tensor in real time during the mission. The implications of this research are significant for future lunar missions, offering enhanced safety and efficiency in vehicle descent and landing operations. Our approach allows for real-time estimation of the lander’s state and for precise execution of maneuvers, verified through complex numerical simulations of the descent, hover, and landing phases. Full article

Driving a motorized wheelchair is not without risk and requires high cognitive effort to obtain good environmental perception. Therefore, people with severe disabilities are at risk, potentially lowering their social engagement, and thus, affecting their overall well-being. Therefore, we designed a cooperative driving system for obstacle avoidance based on a trained reinforcement learning (RL) algorithm. The system takes the desired direction and speed from the user via a joystick and the obstacle distribution from a LiDAR placed in front of the wheelchair. Considering both inputs, the system outputs a pair of forward and rotational speeds that ensure obstacle avoidance while being as close as possible to the user commands. We validated it through simulations and compared it with a vector field histogram (VFH). The preliminary results show that the RL algorithm does not disruptively alter the user intention, reduces the number of collisions, and provides better door passages than a VFH; furthermore, it can be integrated on an embedded device. However, it still suffers from higher jerkiness. Full article

Condition monitoring and preventative maintenance are essential for reliable and efficient operation of permanent magnet synchronous machines driven by inverters. There are two types of industrial inverter drives available: field oriented control and direct torque control. Their compensation nature and control structure are distinct and, therefore, the condition monitoring approach designed for the former control may not be applicable to the latter one. In this paper, we investigate the Motor Voltage Signature Analysis approach for both inverter drives under healthy and faulty conditions. Four typical fault conditions are addressed: turn-to-turn short circuit, high resistance contact, static eccentricity, and local demagnetization. High fidelity cosimulation is developed by coupling the finite element machine model with both control drives. The spectral elements of the commanded stator voltage are utilized as indicators for supervised classification to identify, categorize, and estimate the severity of faults. Linear discriminate analysis, k-nearest neighbor, and support vector machines are the classification techniques used. Results indicate that the condition monitoring based on the Motor Voltage Signature Analysis performs adequately in field oriented control. Nevertheless, the utilized monitoring scheme does not exhibit satisfactory performance in direct torque control owing to the nonlinear characteristics and tolerance nature of this drive. Full article

The doubly fed induction machine (DFIM)-based DC voltage generator is equipped with a stator-connected diode rectifier. The six-pulse diode rectifier as a nonlinear circuit introduces harmonics in the stator and rotor current and distorts the machine stator voltage, as well as the stator flux. This causes electromagnetic torque oscillations and instantaneous power components oscillations. The torque oscillations adversely impact the mechanical parts of the drive-train and oscillations of the p component of instantaneous power influence DC-bus voltage oscillations. The oscillations can be somewhat cancelled by control methods. However, cancellation of electromagnetic torque is not strictly coupled with cancellation of oscillations of the p component of instantaneous power. The paper presents an analysis of influence of the control methods aimed at a reduction of torque oscillations on the output voltage oscillations level in the stand-alone DFIM-based DC voltage generator. Field-oriented control FOC with current controllers and space vector modulation-based direct torque control DTC-SVM with flux module regulation have been compared with control in which electromagnetic torque is one of the commanded variables, whereas the second variable is the dot product of stator flux and rotor current space vectors. The contributions of this paper are the introduction of a new variable in the second control path in the DTC-SVM method instead of flux vector length and the proof that it can reduce torque and DC-bus voltage oscillations in the DFIG-DC system. Additionally, this paper reveals that for proper stator-to-rotor-turns ratio of a doubly fed machine necessary for reduction of the rotor converter power, lower DC-bus voltage can be obtained than is required for full realization rotor side voltage requested by rotor current controllers. This is the reason why, regardless of the control method, torque oscillations cannot be always fully cancelled, and a comparative study of the methods at these conditions has been conducted in simulation and in laboratory tests. Full article

This paper proposes a modified predictive direct torque control (MPDTC) application-specific integrated circuit (ASIC) with multistage hysteresis and fuzzy controller to address the ripple problem of hysteresis controllers and to have a low power consumption chip. The proposed MPDTC ASIC calculates the stator’s magnetic flux and torque by detecting three-phase currents, three-phase voltages, and the rotor speed. Moreover, it eliminates large ripples in the torque and flux by passing through the modified discrete multiple-voltage vector (MDMVV), and four voltage vectors were obtained on the basis of the calculated flux and torque in a cycle. In addition, the speed error was converted into a torque command by using the fuzzy PID controller, and rounding-off calculation was employed to decrease the calculation error of the composite flux. The proposed MDMVV switching table provides 294 combined voltage vectors to the following inverter. The proposed MPDTC scheme generates four voltage vectors in a cycle that can quickly achieve DTC function. The Verilog hardware description language (HDL) was used to implement the hardware architecture, and an ASIC was fabricated with a TSMC 0.18 μm 1P6M CMOS process by using a cell-based design method. Measurement results revealed that the proposed MPDTC ASIC performed with operating frequency, sampling rate, and dead time of 10 MHz, 100 kS/s, and 100 ns, respectively, at a supply voltage of 1.8 V. The power consumption and chip area of the circuit were 2.457 mW and 1.193 mm × 1.190 mm, respectively. The proposed MPDTC ASIC occupied a smaller chip area and exhibited a lower power consumption than the conventional DTC system did in the adopted FPGA development board. The robustness and convenience of the proposed MPDTC ASIC are especially advantageous. Full article

In this paper, a new deadbeat stator current and flux linkage vector control (DB-CFVC) scheme for interior permanent magnet synchronous machines (IPMSM) is proposed. The control structure is simplified by implementing the proposed flux linkage vector control method in the α-β stationary coordinate. Unlike conventional deadbeat methods, the dynamic performance of the proposed DB-CFVC can be enhanced while voltage command saturation and over output current are avoided. This is achieved with a “reinforced” phase angle reference of stator flux linkage vector by considering rotor speed error and maximum voltage to properly enhance the quality of the calculated flux phase angle command. By predicting stator flux linkage and current in the stationary coordinate, the deadbeat direct flux linkage vector control based on the one-step delay compensation strategy becomes straightforward and exhibits low sensitivity to motor parameters compared to conventional methods performed in the rotating frame. Then, by developing a practical and robust hybrid flux linkage observer, the proposed DB-CFVC method can work more reliably and effectively than conventional methods. Simulations and experiments are conducted in a drive system for an IPMSM to evaluate the effectiveness and reliability of the proposed method. Full article

Functional near-infrared spectroscopy (fNIRS) is a comparatively new noninvasive, portable, and easy-to-use brain imaging modality. However, complicated dexterous tasks such as individual finger-tapping, particularly using one hand, have been not investigated using fNIRS technology. Twenty-four healthy volunteers participated in the individual finger-tapping experiment. Data were acquired from the motor cortex using sixteen sources and sixteen detectors. In this preliminary study, we applied standard fNIRS data processing pipeline, i.e., optical densities conversation, signal processing, feature extraction, and classification algorithm implementation. Physiological and non-physiological noise is removed using 4th order band-pass Butter-worth and 3rd order Savitzky–Golay filters. Eight spatial statistical features were selected: signal-mean, peak, minimum, Skewness, Kurtosis, variance, median, and peak-to-peak form data of oxygenated haemoglobin changes. Sophisticated machine learning algorithms were applied, such as support vector machine (SVM), random forests (RF), decision trees (DT), AdaBoost, quadratic discriminant analysis (QDA), Artificial neural networks (ANN), k-nearest neighbors (kNN), and extreme gradient boosting (XGBoost). The average classification accuracies achieved were $0.75\pm 0.04$, $0.75\pm 0.05$, and $0.77\pm 0.06$ using k-nearest neighbors (kNN), Random forest (RF) and XGBoost, respectively. KNN, RF and XGBoost classifiers performed exceptionally well on such a high-class problem. The results need to be further investigated. In the future, a more in-depth analysis of the signal in both temporal and spatial domains will be conducted to investigate the underlying facts. The accuracies achieved are promising results and could open up a new research direction leading to enrichment of control commands generation for fNIRS-based brain-computer interface applications. Full article

The main goal of this paper is to increase the active/reactive power extracted from variable-speed dual-rotor wind power (DRWP) based on doubly-fed induction generators (DFIG) by optimizing its operation using advanced direct vector control. First, the dynamic modeling of different parts of the system is introduced. The DFIG is modeled in the Park reference system. After that, the control techniques are introduced in detail. Direct vector command (DVC) with four-level fuzzy pulse width modulation (FPWM) is used to control the rotor current, thereby controlling the reactive power and active power of the generator. Then, use the neural network design to replace the traditional proportional-integral (PI) controller. Finally, the Matlab/Simulink software is used for simulation to prove the effectiveness of the command strategy using 1.5 MW DRWP. The results show good performance in terms of response time, stability, and precision in following the reference under variable wind speed conditions. In addition, the total harmonic distortion (THD) value of stator current is about 0.13%, being a bit less than other THD values reported in the literature. Full article

A great deal of operational information exists in the form of text. Therefore, extracting operational information from unstructured military text is of great significance for assisting command decision making and operations. Military relation extraction is one of the main tasks of military information extraction, which aims at identifying the relation between two named entities from unstructured military texts. However, the traditional methods of extracting military relations cannot easily resolve problems such as inadequate manual features and inaccurate Chinese word segmentation in military fields, failing to make full use of symmetrical entity relations in military texts. With our approach, based on the pre-trained language model, we present a Chinese military relation extraction method, which combines the bi-directional gate recurrent unit (BiGRU) and multi-head attention mechanism (MHATT). More specifically, the conceptual foundation of our method lies in constructing an embedding layer and combining word embedding with position embedding, based on the pre-trained language model; the output vectors of BiGRU neural networks are symmetrically spliced to learn the semantic features of context, and they fuse the multi-head attention mechanism to improve the ability of expressing semantic information. On the military text corpus that we have built, we conduct extensive experiments. We demonstrate the superiority of our method over the traditional non-attention model, attention model, and improved attention model, and the comprehensive evaluation value F1-score of the model is improved by about 4%. Full article

Currently robotic motion control algorithms are tedious at best to implement, are lacking in automatic situational adaptability, and tend to be static in nature. Humanoid (human-like) control is little more than a dream, for all, but the fastest computers. The main idea of the work presented in this paper is to define a radically new, simple, and computationally lightweight approach to humanoid motion control. A new Proportional-Integral-Derivative (PID) controller algorithm called PID++ is proposed in this work that uses minor adjustments with basic arithmetic, based on the real-time encoder position input, to achieve a stable, precise, controlled, dynamic, adaptive control system, for linear motion control, in any direction regardless of load. With no PID coefficients initially specified, the proposed PID++ algorithm dynamically adjusts and updates the PID coefficients $Kp$, $Ki$ and $Kd$ periodically. No database of values is required to be stored as only the current and previous values of the sensed position with an accurate time base are used in the computations and overwritten in each read interval, eliminating the need of deploying much memory for storing and using vectors or matrices. Complete in its implementation, and truly dynamic and adaptive by design, engineers will be able to use this algorithm in commercial, industrial, biomedical, and space applications alike. With characteristics that are unmistakably human, motion control can be feasibly implemented on even the smallest microcontrollers (MCU) using a single command and without the need of reprogramming or reconfiguration. Full article

Force myography (FMG) signals can read volumetric changes of muscle movements, while a human participant interacts with the environment. For collaborative activities, FMG signals could potentially provide a viable solution to controlling manipulators. In this paper, a novel method to interact with a two-degree-of-freedom (DoF) system consisting of two perpendicular linear stages using FMG is investigated. The method consists in estimating exerted hand forces in dynamic arm motions of a participant using FMG signals to provide velocity commands to the biaxial stage during interactions. Five different arm motion patterns with increasing complexities, i.e., “x-direction”, “y-direction”, “diagonal”, “square”, and “diamond”, were considered as human intentions to manipulate the stage within its planar workspace. FMG-based force estimation was implemented and evaluated with a support vector regressor (SVR) and a kernel ridge regressor (KRR). Real-time assessments, where 10 healthy participants were asked to interact with the biaxial stage by exerted hand forces in the five intended arm motions mentioned above, were conducted. Both the SVR and the KRR obtained higher estimation accuracies of 90–94% during interactions with simple arm motions (x-direction and y-direction), while for complex arm motions (diagonal, square, and diamond) the notable accuracies of 82–89% supported the viability of the FMG-based interactive control. Full article

This paper proposes a simple method to generate stable voltage commands for permanent magnet synchronous motors to achieve low torque ripple based on space vector modulation direct torque control (SVM-DTC). The relationship between applied voltage and electromagnetic torque is first analyzed and the effect of the voltage and flux linkage on torque ripple is discussed. Then, the new method is developed to reduce torque ripple by giving low-fluctuation voltage commands to the voltage source inverter. The magnitude of the voltage command is calculated by avoiding the effect of flux linkage angle that can be estimated inaccurately during motor operation. Therefore, the fluctuation of the voltage command can be significantly reduced. In order to validate the proposed method, Simulink is first used for simulation and the performance of the proposed method is evaluated. It is found that the torque ripple can be substantially reduced while keeping a low switching frequency and improving system response. Then, a hardware-in-the-loop (HIL) technique is applied to test the developed algorithm that is written into a Texas Instruments microcontroller. This validates the simulation results and the proposed method. Full article

This paper proposes a novel control system for the path planning of an omnidirectional mobile robot based on mixed reality. Most research on mobile robots is carried out in a completely real environment or a completely virtual environment. However, a real environment containing virtual objects has important actual applications. The proposed system can control the movement of the mobile robot in the real environment, as well as the interaction between the mobile robot’s motion and virtual objects which can be added to a real environment. First, an interactive interface is presented in the mixed reality device HoloLens. The interface can display the map, path, control command, and other information related to the mobile robot, and it can add virtual objects to the real map to realize a real-time interaction between the mobile robot and the virtual objects. Then, the original path planning algorithm, vector field histogram* (VFH*), is modified in the aspects of the threshold, candidate direction selection, and cost function, to make it more suitable for the scene with virtual objects, reduce the number of calculations required, and improve the security. Experimental results demonstrated that this proposed method can generate the motion path of the mobile robot according to the specific requirements of the operator, and achieve a good obstacle avoidance performance. Full article