Key Words: Neural PID control; adaptive control; electro-hydraulic system; nonlinear systems; MATLAB/Simulink; robustness.
Abstract. This article presents an adaptive Neural PID Optimizer for control of a nonlinear electro-hydraulic servo system. Conventional PID controllers with fixed parameters ensure satisfactory performance only in a limited operating range and exhibit degraded behavior at low rotational speeds due to dead-zone effects and at high speeds due to actuator saturation. To address these limitations, a neural network is employed to adapt the PID gains Kp, Ki, and Kd in real time based on the system state. Simulation studies conducted in MATLAB/Simulink for different operating regimes and load variations demonstrate that the proposed adaptive controller improves dynamic performance, reduces settling time, eliminates steady-state error, and enhances robustness to disturbances. The time evolution of the controller parameters confirms the effective operation of the neural adaptation mechanism. The results validate the suitability of the proposed approach for control of nonlinear electrohydraulic systems with variable operating conditions.
