Performance improvement of AC servo motor PID control with backlash compensation using a Hybrid Genetic Algorithm–Grey Wolf Optimization approach

Abstract

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.