Adaptive neuro sliding mode control of uncertain nonlinear system with dead zone input

Abstract

Aiming at the uncertain nonlinear system with a dead zone input, a design method of adaptive neuro sliding mode control is presented to combine neural network theory with sliding mode control theory. RBF neural networks are used to realize modeling of nondeterministic system. Adaptive laws are derived based on Lyapunov stability theory which guarantees the stability of control system. Theoretical analysis and simulation results indicate that the control approach can be applied to the systems either with or without series nonlinearity and/or dead zone in the input.