Real-Time Implementation of Iterative Learning Control for an Electro-Hydraulic Servo System

Abstract

Electro-hydraulic servo systems (EHSSs) are nonlinear and uncertain due to their inappropriate fluid levels, air temperature, friction, and leakage. The finite-time tracking control is difficult with the use of a proportional, integral, and derivative (PID) controller, which no longer provides adequate and achievable control performance over the whole operating range. This has led to the idea of an iterative learning controller (ILC). An intelligent and memory-based learning control approach that attempts to imitate the human way of thinking. The proposed ILC has an additional learning gain, learning filter, and robustness filter to enhance the finite-time tracking performance and stability improvement. This study is focused on the design of the ILC to regulate the servo spool valve of an EHSS, which in turn controls the displacement of a hydraulic cylinder. In simulation and experimentation, vital parameters such as overshoot and settling time in the varieties of tests, the ILC has shown better results when compared to conventional PID controllers. In step input tracking at different operating points over 0-250 mm, the ILC has 40% less overshoot and settles 12-15 s faster than the PID controller. In sinewave tracking and disturbance rejection, the PID controller performs better than the ILC in integral square error and integral absolute error as the error indices are not considered as the objective function in the design of the controller. During a robustness test, the ILC rejects the uncertainty, which evidences the effectiveness of the proposed controller.