Simulation and experimental study in position control of servo hydraulic system with flexible load using adaptive neural network and differential evolution strategy

Hydraulic position servos with an asymmetrical cylinder are commonly used in industry. These kinds of systems are nonlinear in nature and generally difficult to control. Because of parameters changing during extending and retracting, using constant gain will cause overshoot, poor performance or even loss of system stability. The highly nonlinear behaviour of these devices makes them ideal subjects for applying different types of sophisticated controllers. This paper is concerned with a second order adaptive model reference and an artificial neural network controller to position tracking of a servo hydraulic with a flexible load. In present study, a neural network with two outputs is presented. One of the outputs of neural network is used for system's dynamic compensator and another one for gain scheduling controller. To avoid the local minimum problem, Differential Evolution Algorithm (DEA) is used to find the weights and biases of neural network. The proposed controller is verified with a common used p-controller. The simulation and experimental results suggest that if the neural network is chosen and trained well, it improves all performance evaluation criteria such as stability, fast response, and accurate reference model tracking in servo hydraulic systems.