Integral fuzzy sliding mode controller for hydraulic system using neural network modelling

Abstract

In this paper, a hydraulic motor controller is designed with a fuzzy supported integral sliding mode algorithm. The hydraulic system used in the study was modeled using artificial neural networks. Ability of handling nonlinearity of systems makes sliding mode controller to be a good choose for this system. It is thought that the robustness of the system against uncertainties can be achieved with the help of an integral sliding mode controller. The basic concept of the suggested control method is to use fuzzy logic for adaptation of the integral sliding mode control switching gain. Such adjustment reduces the chattering that is the most problem of classical sliding mode control. The equivalent control is computed with utilizing the radial basis function neural network. The simulation results of the presented method are compared with the results of the PID controller whose parameters were obtained by means of a genetic algorithm (GA) and particle swarm optimization (PSO). It proved that it is more efficient to control the hydraulic system with integral fuzzy sliding mode control using neural network.