Variable structure controller with neural compensator

Abstract

This paper proposes a new variable structure controller combined with a multilayer neural network using an error back-propagation learning algorithm. The neural network acts as a compensator for a conventional variable structure controller in order to improve the control performance when the initial assumptions of the uncertainty bounds of the system parameters are violated. Also, the proposed controller can reduce the steady-state error of a conventional variable structure controller using the boundary layer technique. Computer simulation results show that the proposed method is eVective in controlling dynamic systems with unexpected large uncertainties.