Comparative Analysis of Intelligent PID Tuning Methods for Position Tracking in Mismatched Electro-Hydraulic Actuator

Electro-hydraulic actuators (EHAs) are widely applied in industrial motion control systems due to their high-power density and fast response. However, external disturbances make accuracy of the position control of EHAs challenging. Proportional–Integral–Derivative (PID) controllers are commonly used in practice, but their performance depends heavily on the tuning method. This study presents a comparative evaluation of four PID tuning techniques-Ziegler-Nichols (ZN), Particle Swarm Optimization (PSO), Adaptive Particle Swarm Optimization (APSO), and Fuzzy-PID (FPID) applied to a mismatched EHA system under mismatched operating conditions arising from external load disturbances, which enter the actuator dynamics through a mechanical state different from the control input channel rather than through the input voltage, thereby constituting a mismatched disturbance in the control-theoretic sense. All controllers are implemented in MATLAB/Simulink and tested using a step input reference under four external disturbance conditions. Performance is evaluated based on trajectory tracking accuracy, settling time, overshoot, steady-state error, and sum of squared error (SSE). Simulation results show that the conventional ZN-PID controller suffers from large overshoot, longer settling time, and higher SSE, particularly under increased disturbances. PSO-PID and APSO-PID improve transient response and reduce SSE; however, their performance degrades under severe disturbances. In contrast, the Fuzzy-PID controller consistently achieves the best overall performance. Compared with ZN-PID, the Fuzzy-PID reduces SSE by approximately 50–72% across all disturbance cases, while also yielding faster settling time, negligible overshoot, and minimal steady-state error. These results demonstrate that intelligent tuning approaches significantly enhance EHA position control performance. Among the evaluated methods, Fuzzy-PID provides the highest robustness and tracking accuracy under mismatched and disturbance-prone conditions, making it well suited for practical industrial EHA applications.