Speed Tracking Accuracy Research Articles

Experimental Implementation of a TD3 Agent Based Speed Controller for Direct Torque Control of PMSM Drives

This manuscript presents a novel control approach for Permanent Magnet Synchronous Motors (PMSMs) by integrating the widely used Direct Torque Control (DTC) method with Deep Reinforcement Learning (DRL), specifically the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. The TD3 algorithm is well-suited to address the challenges posed by high-dimensional state and action spaces in PMSM drives. The conventional Proportional–Integral (PI) controller in the outer loop of DTC is replaced with the DRL based TD3 agent to overcome the limitations of traditional PI controllers, such as model dependency and complex parameter tuning. The DRL technique allows the agent to learn optimal control policies from experience, making it suitable for complex and nonlinear control problems. Extensive simulation studies demonstrate the effectiveness of the TD3-based control in achieving accurate speed tracking under varying operating conditions. Real-time experimental validation using a TMS320F28379D digital signal processor confirms the feasibility of the proposed DRL based approach. The research offers new insights into improving the performance of PMSM drives and paves the way for future advancements in electric drive control.

Fast fixed-time extended state observer based command filtering backstepping control of free-flying flexible-joint space robots

System parameter perturbations, external disturbances, and input saturation issues seriously affect the on-orbit operation precision and rapid response capabilities of the free-flying flexible-joint space robots (FFSR). To this end, a fixed-time extended state observer (ESO) based non-singular command filtering backstepping controller is proposed. The fast fixed-time ESO is designed to estimate the parameter perturbations and external disturbances. To avoid the “computational explosion” caused by multiple derivatives of the virtual control law in backstepping control, a second-order command filter is employed to obtain the derivative of virtual control laws. Meanwhile, to reduce the filtering errors induced by the command filter, a non-singular fixed-time filtering error compensation algorithm is developed. Furthermore, considering the constraints of control moment gyroscope (CMG) and joint motors, an anti-saturation compensation algorithm is introduced to drive the system out of the saturated region rapidly, thus reducing actuator saturation effects on the control system performance. Theoretical analysis shows the proposed controller can ensure the system tracking error converges to any arbitrarily small neighborhood of the origin within fixed time. Simulation results demonstrate that the designed non-singular fixed-time command filtering backstepping controller exhibits fast convergence speed and high tracking accuracy.

Research on Optimization of Intelligent Driving Vehicle Path Tracking Control Strategy Based on Backpropagation Neural Network

To enhance path tracking precision in intelligent vehicles, this study proposes a lateral–longitudinal control strategy optimized with a Backpropagation (BP) neural network. The strategy employs the BP neural network to dynamically adjust prediction and control time-domain parameters within an established Model Predictive Control (MPC) framework, effectively computing real-time front-wheel steering angles for lateral control. Simultaneously, it integrates an incremental Proportional–Integral–Derivative (PID) approach with a meticulously designed acceleration–deceleration strategy for accurate and stable longitudinal speed tracking. The strategy’s efficiency and superior performance are validated through a comprehensive CarSim(2020)/Simulink(2020b) simulation, demonstrating that the proposed controller adeptly modulates control parameters to adapt to various road adhesion coefficients and vehicle speeds. This adaptability significantly improves tracking and driving dynamics, thereby enhancing accuracy, safety, stability, and real-time responsiveness in the intelligent vehicle tracking control system.

Multi-Parameter Fuzzy-Based Neural Network Sensorless PMSM Iterative Learning Control Algorithm for Vibration Suppression of Ship Rim-Driven Thruster

Aiming to reduce motor speed estimation and torque vibration present in the permanent magnet synchronous motors (PMSMs) of rim-driven thrusters (RDTs), a position-sensorless control algorithm using an adaptive second-order sliding mode observer (SMO) based on the super-twisting algorithm (STA) is proposed. In which the sliding mode coefficients can be adaptively tuned. Similarly, an iterative learning control (ILC) algorithm is presented to enhance the robustness of the velocity adjustment loop. By continuously learning and adjusting the difference between the actual speed and given speed of RDT motor through ILC algorithm, online compensation for the q-axis given current of RDT motor is achieved, thereby suppressing periodic speed fluctuations during motor running. Fuzzy neural network (FNN) training can be used to optimize the STA-SMO and ILC parameters of RDT control system, while improving speed tracking accuracy. Finally, simulation and experimental verifications have been conducted on the vector control system based on the conventional PI-STA and modified ILC-STA. The results show that the modified algorithm can effectively suppress the estimated speed and torque ripple of RDT motor, which greatly improves the speed tracking accuracy.

A Fuzzy-Based Proportional–Integral–Derivative with Space-Vector Control and Direct Thrust Control for a Linear Induction Motor

In this study, the analysis and control of a multi-phase linear induction motor loaded with a variable mechanical system are carried out. Mathematical models are established, and simulation results are analyzed for an improved proportional–integral–derivative controller with closed-loop vector control for PLIM. To make the PID controller more responsive to load thrust disturbances, a fuzzy PID load thrust observer was developed. The FPID is similarly based on space-vector modulation DTC technology to regulate the PLIM’s speed, flux, and thrust. The FPID output is used to calculate the reference thrust force, which is compared to the actual thrust value to calculate the second error. To maintain the linear speed of the PLIM at the specified reference values and at different load values, the FPID controller settings are adjusted. Four indicators were used to compare the capabilities of the FPID controller with those of the conventional PID controller in order to evaluate the performance of PLIM in both cases. These indices represent the individual SSE for each operational phase and the total SSE for the entire loading period. According to the simulation results, the FPID works better than a regular PID when used to adjust the operation of DTC-SVM to drive a PLIM to improve the overall system performance. The simulation results using MATLAB Simulink for a PLIM-drive system show that the proposed FPID control provides improved control behavior and operating performance with fast and accurate speed tracking.

Backstepping Sliding Mode Control Algorithm for Unmanned Aerial Vehicles Based on Fractional-Order Theory

Aiming at the problems of slow convergence speed and low tracking accuracy in attitude control and position tracking of quadrotor unmanned aerial vehicles (UAVs). This paper combines the fractional-order calculus theory with the backstepping sliding mode control algorithm, using the backstepping control to compensate for the nonlinearity of the system and the fractional-order theory to eliminate the jitter brought about by the sliding mode control, and proposes a new fractional-order backstepping sliding mode control strategy for the trajectory tracking control of the quadrotor UAV. The proposed fractional-order sliding mode surface increases the control flexibility and improves the robustness and anti-interference ability of the system to some extent. The stability analysis of the system is carried out using the Lyapunov stability theory, and the results prove the stability of the proposed controller. Finally, the effectiveness and feasibility of the proposed method are verified by comparing it with the traditional backstepping sliding mode controller. The simulation results show that the fractional-order reverse-step sliding mode control algorithm proposed in this paper is significantly better than other control algorithms in terms of convergence speed and also has a certain degree of superiority in terms of error elimination.

Wet clutch pressure hysteresis compensation control under variable oil temperatures for electro-hydraulic actuators

The hysteresis characteristics and oil temperatures of wet clutch electro-hydraulic actuators have a significant impact on clutch pressure tracking performance, thereby influencing the launch, shift, and mode transition performance of both fuel and hybrid electric vehicles. In this study, we introduce a novel compensator for wet clutch pressure hysteresis that effectively adapts to variations in oil temperature. Experimental investigations were conducted to examine the hysteresis characteristic of the electro-hydraulic actuator and its susceptibility to changes in oil temperature. Subsequently, a temperature-dependent modified generalized Prandtl–Ishlinskii (TDMGPI) hysteresis model that accounts for the influence of oil temperature was established. To mitigate the effects of oil temperature fluctuations on the controller, we developed a fuzzy proportional-integral-derivative (FPID) controller, which dynamically adjusts controller gain. Furthermore, a feedforward-feedback controller for clutch pressure was established, incorporating the inverse solution of the TDMGPI model as the feedforward component and the FPID controller as the feedback element. The experiments were conducted at various oil temperatures, revealing that the proposed controller significantly enhances response speed and pressure tracking accuracy, maintaining good control performance across the tested range of oil temperatures.

Improved active disturbance rejection speed control for autonomous driving of high-speed train based on feedforward compensation.

Due to the complex and changeable train operation environment and the unstable and time-varying parameters, accurate modeling is limited. Therefore, a modified active disturbance rejection control algorithm based on feedforward compensation (FC-MADRC) is proposed targeting the speed control problem of trains under the circumstances of external disturbances, which reduces the dependence on the train model. Firstly, the state space equation is established based on the single-particle mathematical model of the train, and all the running resistances are regarded as disturbances. Secondly, the FC-MADRC algorithm is designed. Based on the terminal attractor function and the novel Sigmoid function, an improved tracking differentiator (ITD) is designed. An improved fal (nsfal) function with better smoothness is constructed by using the properties of the Dirac δ function, and an ameliorative nonlinear state error feedback (ANLSEF) and a modified extended state observer (IESO) are designed based on the nsfal function. Furthermore, based on the thought of PID, the integral term of error is introduced into ANLSEF for the nonlinear operation to reduce the steady-state error of train speed tracking. In order to promote the robustness and control accuracy of the system, the feedforward compensation term and disturbance compensation term are combined to perform dynamic compensation for disturbances in real time. Finally, the simulation is carried out with CRH380A train data. The results indicate that compared with conventional ADRC and 2DOF-PID, FC-MADRC has the more vital anti-disturbance ability and higher tracking accuracy. FC-MADRC has the advantages of solid anti-disturbance, fast response, and high tracking accuracy. Under the premise of external disturbance, it can still achieve accurate speed tracking under different road conditions.

A novel sliding mode control with MRAS inertia identification for permanent magnet synchronous motors

To enhance the anti-inertia disturbance ability of permanent magnet synchronous motor (PMSM) speed system, an adaptive sliding mode control with inertia identification is proposed. A novel sliding mode control (NSMC) based on a new reaching law coupled with model reference adaptive system (MRAS) inertia identification is realized the adaptive control, named MRAS+NSMC. In the NSMC construct process, an integral sliding mode surface and a variable speed reaching law are introduced to avoid speed differentiation and improve dynamics, respectively. And the new reaching law imported a successive sigmoid( s) to replace the traditional sign( s) to suppress chattering phenomena. For the problem that the performance deteriorated by rotational inertia variation caused by load changes, the inertia is estimated in real time according to the MRAS theory, and the identification value is updated to the NSMC controller to realize adaptive MRAS+NSMC speed control. Experimental results show that the proposed adaptive MRAS+NSMC control has a faster speed response, and the speed response time is reduced from 85 to 49 ms compared with conventional SMC control. In addition, it has strong robustness to inertia disturbances and high speed tracking accuracy. Compared with conventional SMC, the speed tracking accuracy of proposed MRAS+NSMC is increased from 12% to 4%. This makes the proposed MRAS+NSMC control has great potential practical significance for speed control of PMSM.

An Improved Integral Sliding Mode Control for PMSM Drives Based on New Variable Rate Reaching Law With Adaptive Reduced-Order PI Observer

This paper focuses on speed control in a permanent-magnet synchronous motor (PMSM). To promote the speed tracking performance and anti-disturbance property of PMSM speed regulation system against various unknown disturbances, such as parameter mismatch, load torque variation, friction force, and so on, an improved integral sliding mode control (IISMC) method based on a new variable rate reaching law (NVRRL) and an adaptive reduced-order proportional integral observer (AROPIO) is proposed. The NVRRL method can reduce chattering, and convergence time of the system state at the same time compared to the existing sliding mode reaching law. An anti-windup sliding mode surface is proposed to overcome the overshoot problem of the integral sliding mode surface when the reference speed is large and rises in steps or large ramps. To further improve the system tracking accuracy and anti-disturbance ability, an AROPIO is used to estimate lumped disturbances and provides feedforward compensation for the IISMC. The simulation and experimental results show that the proposed IISMC+AROPIO method not only achieves high speed tracking accuracy and fast convergence speed but also achieves strong anti-interference ability.

Additive-state-decomposition-based model predictive tracking control for PMSM servo system with multiple disturbances

This paper presents an additive-state-decomposition-based model predictive tracking control and disturbance rejection method for a permanent magnet synchronous motor (PMSM) servo system subject to unknown parameter perturbations, unmodeled dynamics, and time-varying load torque. The basic idea of this method is to equivalently decompose the original system into a primary system for handling the tracking control subproblem and a secondary system for dealing with the robust stabilization subproblem. A model predictive controller is designed for the primary system to achieve high-accuracy tracking of the reference speed. As for the secondary system, a novel high-order generalized extended state observer (HGESO) is constructed to estimate the multiple disturbances simultaneously, and a state feedback control law incorporating a disturbance compensator is developed to eliminate the adverse effect of the multiple disturbances on the system output. By combining the control inputs of the two subsystems together, the control objectives of the original system can be achieved. Both the stability criterion and design procedure of the closed-loop control system are developed. Finally, hardware-in-the-loop-based comparative experiments are conducted to demonstrate that the proposed method effectively suppresses the influence of the multiple disturbances on motor speed tracking accuracy and that the control system has both satisfactory dynamic performance and robustness.

Optimal Tuning of Fractional Order Sliding Mode Controller for PMSM Speed Using Neural Network with Reinforcement Learning

An improved fractional-order sliding mode control (FOSMC) for PMSM is presented in this study to set the unavoidable parameters and to improve permanent magnet synchronous motors (PMSMs) drive performance, such as current and speed tracking accuracy. To determine the optimal parameters of the FOSMC for control speed in a PMSM drive, a neural network algorithm with reinforcement learning (RLNNA) is proposed. The FOSMC parameters are set by the ANN algorithm and then adapted through reinforcement learning to enhance the results. The proposed controller using RLNNA based on fractional-order sliding mode control (RLNNA-FOSMC) can drive the motor speed to achieve the referred value in a finite period of time, leading to faster convergence and improved tracking accuracy. For a fair comparison and evaluation, the proposed RLNNA-FOSMC is compared with conventional FOSMC by applying the integral of time multiplied absolute error as an objective function. The most commonly used objective functions in the literature were also compared, including the integral time multiplied square error, integral square error, and integral absolute error. To validate the performance of the RLNNA-FOSMC speed controller, different scenarios with different speeds steps were carried out. The computational results are promising and demonstrate the effectiveness of the proposed controller. Overall, the proposed RLNNA-FOSMC controller for the PMSM speed control system performed better than conventional FOSMC in numerical simulations.

Trajectory-Regulation-Based Segmented Control for Dead Center Positions Tracking of Free-Piston Linear Generator

Free-piston linear generator (FPLG), as a novel energy converter, has attracted wide attention due to its unique operating characteristics. However, the high-precision and robust tracking of the dead center positions in the presence of frequent combustion variation is still a tricky problem. In this respect, in this article, we present a trajectory-regulation-based segmented control (TRBSC) to actively control the linear electric machine (LEM) of FP LG. The target stroke is divided into the high-speed zone and low-speed zone, where active tracking of the input reference trajectory of LEM and free damped motion are performed separately. Especially, the input trajectory is selected as a variable sinusoidal reference velocity profile, which is adjusted by the combinations of the dead center errors. The segmented control decouples the specific interaction between the dead center positions and velocity curve, reducing the heavy dependence of the algorithm on speed tracking accuracy. A full theoretical analysis is provided with emphasis on the regulation rules of the reference speed profile based on the modeling of the displacement profiles. The performance of the TRBSC is experimentally verified on an 18 kW permanent-magnet linear synchronous motor setup in motoring mode.

Analysis of Explicit Model Predictive Control for Track-Following Servo Control of Lunar Gravity Compensation Facility

The Lunar Gravity Compensation Facility (LGCF) is a critical component in ground tests for a crewed lunar roving vehicle (CLRV). The track-following servo subsystem’s performance is of critical importance in the LGCF, as it needs to achieve high-precision tracking of the CLRV’s fast, wide range of motion in the horizontal direction. The subsystem must also operate within various constraints, including those related to speed, acceleration, and position. These requirements introduce new challenges to both the design and control of the subsystem. To tackle these challenges, this paper employs a Permanent-Magnet Synchronous Motor (PMSM) vector control method based on Space Vector Pulse Width Modulation (SVPWM) to achieve accurate speed tracking. Additionally, this paper presents an Explicit Model Predictive Control (EMPC) strategy for precise position servo control of the track-following system under multi-parameter constraints. The simulation model of the track-following servo subsystem is established based on the above methods. The simulation results demonstrate that the position tracking error of the gravity compensation system, constructed using the above method combined with EMPC control, is less than 0.2 m. The control performance of the EMPC is significantly better than those of the PI and LQI controllers. The influence of errors on the drawbar pull is within 12.5%, and its effect on the compensation force is negligible. These results provide theoretical support for the design of a track-following servo subsystem.

A bi-level control framework with temperature rise inference and intelligent decision-making longitudinal control on downgrade hydrodynamic braking process

Temperature rise is a salient characteristic of the braking device of heavy-duty vehicles (HDVs) during long downhill braking. The temperature-independent effect on braking performance is an inevitable challenge for the longitudinal controller, which also causes the overheating of mechanical or hydrodynamic braking devices and may bring about brake failures for heavy-duty vehicles. To this end, a universal Bi-level control framework combining a temperature hierarchy system performance prediction method and a deep reinforcement learning (DRL)-based controller is proposed for long downhill braking of heavy-duty vehicles. Firstly, the temperature-independent characteristic is clustered to predict the braking performance under various rotating speeds. Secondly, a data-driven model for temperature rising is built for the long-time braking thermal prediction and estimates the safety remaining braking time using the auxiliary braking. Thirdly, a temperature-hierarchy environmental perceptive control framework with Double Deep Q Network (DDQN) algorithm is exploited to achieve the target speed tracking accuracy. Thermal safety is ensured with the application of fast calculating for the thermal rising, along with the effective estimation of remaining braking performance on endurance braking. The proposed Bi-level longitudinal controller is compared with the average-temperature strategy and the classic PID strategy to validate its superiority in terms of speed-tracking accuracy on robust conditions. The simulation results show that the proposed strategy improves the speed tracking accuracy by 37.31% on constant slope conditions and 68.11% on varying slope conditions compared with the classic PID strategy. Furthermore, a processor-in-the-loop test experiment verifies its real-time application.

Online Running-Gait Generation for Bipedal Robots with Smooth State Switching and Accurate Speed Tracking.

Smooth state switching and accurate speed tracking are important for the stability and reactivity of bipedal robots when running. However, previous studies have rarely been able to synthesize these two capabilities online. In this paper, we present an online running-gait generator for bipedal robots that allows for smooth state switching and accurate speed tracking. Considering a fluctuating height nature and computational expediency, the robot is represented by a simplified variable-height inverted-pendulum (VHIP) model. In order to achieve smooth state switching at the beginning and end of running, a segmented zero moment point (ZMP) trajectory optimization is proposed to automatically provide a feasible and smooth center-of-mass (CoM) trajectory that enables the robot to stably start or stop running at the given speed. To accurately track online the desired speed during running, we propose an iterative algorithm to compute target footholds, which allows for the robot to follow the interactive desired speed after the next two steps. Lastly, a numerical experiment and the simulation of online variable speed running were performed with position-controlled bipedal robot BHR7P, and the results verified the effectiveness of the proposed methods.

A novel trajectory tracking control approach for uncertain 6-DOF manipulators based on fuzzy sliding mode of radial basis function neural network

Due to the influence of uncertain external disturbance and model error, the trajectory tracking of manipulators has some problems such as inaccuracy, low speed and severe chattering. To solve the problems mentioned, a sliding mode control method combined fuzzy controller and Radial Basis Function Neural Network (RBFNN) was proposed. The RBFNN was used to approximate the equivalent system model of manipulators, and the weights of their hidden layers were updated adaptively online. The robust item with the form of sliding mode had been set to compensate the uncertain external disturbances or the approximation errors of the RBFNN. A fuzzy controller was used to adjust the switching gain online adaptively and to solve the chattering of the sliding mode control. The stability and finite time convergence of the closed-loop system had been analyzed by using Lyapunov method. Finally, simulation experiments about trajectory tracking were implemented by MATLAB/SIMULINK, which shows that the proposed method can improve the position-tracking accuracy by more than 40%, the speed-tracking accuracy by more than 70%, and the cumulative-error accuracy by more than 40% compared with the comparison method. Moreover, there is no chattering. The proposed method has been proved to be efficient.

Driving Torque Control of Dual-Motor Powertrain for Electric Vehicles

This paper investigates the driving torque control method for the dual-motor powertrain in electric vehicles (EVs) to achieve the performance of accurate vehicle speed tracking, seamless driving mode shift, and high energy efficiency. The configuration of the dual-motor powertrain is based on the parallel axle transmission structure, which does not contain any clutch or synchronizer. The powertrain provides three driving modes that are two single-motor driving modes and one dual-motor combined driving mode. A detailed dynamic model of the dual-motor powertrain is built to simulate the dynamic response of an EV. An energy management strategy (EMS) is used to select the driving mode and determine the ideal driving torque of two motors. The dynamic control strategy tries to track the ideal vehicle speed when uncertain parameters existed and avoid power interruption or impact during the mode shift. Three dynamic control strategies are proposed, which are the backward dynamic control strategy (BDCS), combined forward and backward dynamic control strategy (CFBDCS), and nested forward and backward dynamic control strategy (NFBDCS). The simulation results demonstrate that the NFBDCS has the best comprehensive performance in vehicle speed tracking, seamless mode shift, and good system energy efficiency.

Adaptive fuzzy sliding mode control for high‐speed train using multi‐body dynamics model

In this paper, an equivalent multi-body dynamics model of high-speed train with uncertain parameter is established. Based on this detailed model and neural network minimum parameter learning algorithm, an adaptive sliding mode control method is designed for speed and position tracking control of high-speed train, and the stability of the proposed control method is proved strictly. Then an adaptive fuzzy sliding mode control method (AFSMC) is presented to avoid chattering caused by excessive robust switching gain. Finally, numerical tests are carried out on the proposed AFSMC under different scenarios and operation strategies, and model reference adaptive control (MRAC), PID control and sliding mode control are also tested under the same condition for performance comparison. The results show that the proposed control method is superior to the currently used method. More specifically, compared with MRAC and PID, the average speed tracking accuracy of AFSMC is improved by 0.25 km/h and 0.18 km/h, and the average position tracking accuracy is improved by 0.68 m and 0.7 m, respectively.

Ultra-local model-free speed prediction control based on high-order sliding mode compensation for PMSM drives

The model predictive control (MPC) is widely used in permanent magnet synchronous motor (PMSM) motion systems because of its simple structure, easy implementation, and reasonable control effect to realize accurate speed tracking. However, MPC relies on the mathematical model of a PMSM. During motor operation, parameter drifts will occur and the model will be mismatched. Furthermore, motor operation may be affected by known and unknown disturbances, resulting in a degraded control performance. To solve this problem, ultra-local model-free speed prediction control (MFSPC), based on high-order sliding mode compensation (HOSMC), is proposed. Only the output and input of the speed loop are used to establish MFSPC and no system parameters are considered. HOSMC shows good control performance under nonlinearity and is not sensitive to parameter changes and external disturbances, which improves the robustness and anti-interference performance of the control system. Therefore, the current output is composed of two parts: an MFSPC output current and an HOSMC output current. The experimental results show that the method has strong robustness and anti-interference ability, and it can achieve a rapid dynamic response, track rated speed, reduce current and speed ripples, and suppress chattering.