Jul 2026· Transactions of the Institute of Measurement and Control· 0 citations· 16 references
Abstract
To improve the tracking performance and dynamic response of a permanent magnet synchronous motor servo system, this study investigates a performance-oriented parameter-tuning framework for the controllers in the servo loops. Rather than proposing a new control structure, the work focuses on the formulation and solution of the tuning problem under stringent requirements on both transient and steady-state performance. A comprehensive optimization criterion is established by jointly considering overshoot, settling time, and steady-state error from a global performance perspective. Based on this criterion, an improved multi-verse optimizer with nonlinear parameter scheduling and an inertia-weight mechanism is employed to search for controller parameters with a better balance between exploration and exploitation. The proposed tuning framework is evaluated on a MATLAB/Simulink co-simulation platform under tracking and load-disturbance conditions. Simulation results indicate that, compared with the baseline tuning method, the optimized controller parameters can improve dynamic response characteristics and reduce overshoot and steady-state error. Nevertheless, the present validation is limited to co-simulation, and hardware-level experimental verification will be pursued in future work to further assess practical applicability.
This paper presents a comprehensive experimental and simulation-based investigation of intelligent optimization techniques for PID-controlled AC servo drive systems. Three optimization approaches, namely the Genetic Algorithm (GA), Grey Wolf Optimization (GWO), and a Hybrid Genetic Algorithm Grey Wolf Optimization (HGAGWO), are employed to determine the optimal PID controller parameters for high-precision speed and position control under varying operating conditions and load disturbances. The proposed framework combines advanced optimization techniques with experimental validation to provide a reliable assessment of controller performance in practical industrial environments. The experimental platform consists of a programmable logic controller (PLC), a human-machine interface (HMI), an AC servo drive, and a high-resolution external encoder, while MATLAB/Simulink is utilized to develop and validate the dynamic model. Controller performance is systematically evaluated using widely accepted dynamic performance indices, including rising time, settling time, overshoot, steady-state error, and tracking capability under different operating scenarios. The controller optimization study addresses backlash, one of the most significant nonlinearities affecting servo drive accuracy. A backlash compensation strategy is implemented and experimentally verified, demonstrating a noticeable improvement in positioning precision and motion stability. Comparative simulation and experimental results confirm that the optimized PID controllers significantly enhance the transient response, disturbance-rejection capability, tracking accuracy, and overall system robustness compared with conventional PID tuning methods. Among the investigated optimization techniques, the proposed HGAGWO algorithm effectively combines the global exploration capability of GA with the fast convergence characteristics of GWO, producing superior optimization accuracy, faster convergence, and more reliable controller tuning. The close agreement between simulation and experimental results further validates the effectiveness and practical applicability of the proposed methodology. Therefore, the integration of intelligent PID optimization with backlash compensation provides a robust and efficient motion control solution for high-performance industrial servo systems operating under nonlinearities, load variations, and parameter uncertainties.
Shereen A. Fayad, M. Shaban, Mohamed I Attia et al.· Scientific Reports· 0 citations
Permanent Magnet Synchronous Motors (PMSMs) are widely employed in electric vehicle (EV) propulsion systems because of their high efficiency, high power density, and superior dynamic performance. The performance of field-oriented control (FOC)-based PMSM drives strongly depends on accurate tuning of the proportional–integral (PI) controllers governing the speed and current loops. Conventional tuning approaches often optimize a single performance index and therefore fail to simultaneously enhance the dynamic behavior of all control loops. This paper proposes a multi-error Integral of Time-weighted Absolute Error (ITAE)-based optimization framework for simultaneous tuning of the PI controllers by minimizing a composite objective function that incorporates the time-weighted absolute errors of the rotor speed, q-axis current, and d-axis current. To validate the effectiveness and optimizer independence of the proposed framework, five metaheuristic optimization algorithms—Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gray Wolf Optimizer (GWO), Gazelle Optimization Algorithm (GOA), and White Shark Optimization (WSO)—are evaluated under identical optimization settings. MATLAB/Simulink simulations are performed for reference-speed tracking, load disturbance rejection, and variable-speed operation. The results demonstrate that the proposed optimization framework consistently improves tracking accuracy and dynamic response regardless of the selected optimizer, while WSO provides the best overall performance. In the variable-speed tracking scenario, WSO achieved the lowest RMSE of 0.96 rad/s and the minimum ITAE value of 0.1716, confirming its effectiveness as the most suitable optimizer for the proposed framework in high-performance PMSM drive applications.
Ahmed Mashaly, Mohamed Elgohary, R. El-Sehiemy· Machines· 0 citations
The permanent magnet synchronous motor (PMSM) has been extensively applied in high-performance drive systems because of its high efficiency, compact construction, and superior dynamic performance. However, the design and implementation of traditional sliding mode control (SMC) systems have to be very dependent on the parameter selection, which could possibly lead to the increase of chattering and decreased adaptability under different operating conditions. In order to solve these problems, an SMC controller parameter optimization method based on reinforcement learning (RL) is put forward in this paper. The proposed RL-based SMC controller parameter optimization algorithm integrates RL into the sliding mode control process, and the adaptive adjustment of the control parameters can be realized through feedback from the performance of the controlled system. Specifically, the system states consist of the speed tracking error, the current error, and the sliding surface characteristic; the optimization goal contains the tracking accuracy, the convergence performance, and the chattering suppression. Simulation experiments on MATLAB/Simulink are performed to validate the effectiveness of the proposed method. The results show that, compared with the traditional SMC, the RL-based adaptive SMC control scheme can achieve good dynamic performance, less steady-state error, chattering reduction, and disturbance rejection.
Xiao-Long Tian, Peng Zhang, Hai-tao Li et al.· 電腦學刊· 0 citations
Nowadays, a lot of low-power marine electric motors are utilized in ship propulsion systems; these motors must meet three criteria: energy-saving, precise speed control, and steady operation. To address the adaptability and disturbancerejection flaws of the former, a Data-Driven Control Strategy based on Proximal Policy Optimization (PPO) is presented. Create a dynamic state-space model for the marine motor that incorporates the required mechanical and electrical properties together with realistic load variations. Stable velocity control and low power consumption are the goals of the PPO control system's reward function. The performance of a PPO controller and a conventional PID approach under different reference-tracking and disturbance situations will be experimentally validated using a MATLAB/Simulink simulation platform. The aforementioned findings demonstrate that the PPO approach has enhanced load-disturbance rejection and decreased the system's rise time, overshoot, and steady-state error. The aforementioned quantitative investigations verify that the PPO controller outperforms conventional techniques in terms of both control efficiency and transient and steady-state response characteristics. The aforementioned findings demonstrate that reinforcement learning can be used to dynamically modify the speed of marine motors, resulting in the realization of a more reliable and effective marine propulsion system.
Guang-Lei Li, Yunkai Huang· International Conference on...· 0 citations
Brushless DC (BLDC) motors have gained widespread acceptance in electric vehicles, industrial automation, robotics, and renewable energy applications owing to their superior efficiency, reliability, and superior dynamic characteristics. However, achieving accurate speed regulation under diverse system conditions remains a challenging task driven by the nonlinear nature of motor dynamics. This research examines the implementation and real-time implementation of an ANN-assisted PID-controller for BLDC motor speed regulation. The proposed controller utilizes an ANN to adaptively tune the PID parameters according to the motor operating conditions. Initially, the controller performance is verified through MATLAB/Simulink simulation. Subsequently, the trained ANN model is deployed on a microcontroller-based hardware platform for experimental validation. The response of the implemented system controller is evaluated and compared with a conventional PID controller using speed response characteristics and error-based performance indices. Experimental results demonstrate improved speed tracking, lower overshoot and shorter settling duration and enhanced robustness under varying operating conditions. The study confirms the practical feasibility of ANN-assisted intelligent control strategies for real-time BLDC-motor drive applications.
N. D., Vasudeamurthy. S· 2026 International Conferenc...· 0 citations
This study proposes a gain-scheduled linear parameter-varying (LPV) control strategy for rotor speed regulation of a permanent magnet synchronous generator (PMSG) in wind turbine conversion systems (WTCS). The nonlinear electromechanical dynamics are reformulated into a speed-dependent LPV model over the operating region. To support reference generation without direct aerodynamic torque measurement, a first-order aerodynamic torque observer is introduced. Based on a set of local linear quadratic regulators designed at selected operating points, a smooth gain-scheduling law is constructed through a neural-network-based convex interpolation mechanism. The resulting controller preserves continuity across operating conditions and improves dynamic response compared with a conventional PI-PI strategy. Closed-loop stability is discussed using a common Lyapunov theory for the local closed-loop models, and simulation studies under nominal and parameter-perturbed conditions are carried out to evaluate the proposed method. The simulation results indicate that the proposed strategy can provide satisfactory performance.
A. T. Nguyen, Q. T. T. Nguyen, L. Tran et al.· 2026 11th International Conf...· 0 citations
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