Designing a Neuro-Sliding Mode Controller for Networked Control Systems with Packet Dropout

Abstract

This paper addresses control design in networked control system by considering stochastic packet dropouts in the forward path of the control loop. The packet dropouts are modelled by mutually independent stochastic variables satisfying Bernoulli binary distribution. A sliding mode controller is utilized to overcome the adverse influences of stochastic packet dropouts in networked control systems. Firstly, to determine the parameters of switching function used in the sliding mode control design, an improved genetic algorithm is applied. The proposed improved genetic algorithm provides a fast convergence rate and a proper dynamic performance in comparison with conventional genetic algorithms especially in online control applications. Then, an adaptive neural sliding mode control based on radial-basis function neural network approximation is proposed to eliminate chattering phenomenon in the sliding mode control. A numerical example is given to illustrate the effectiveness of the proposed controller in networked control systems. The results show that the proposed controller provides high-performance dynamic characteristics and robustness against plant parameter variations and external disturbances.