Adaptive neural sliding mode compensator for a class of nonlinear systems with unmodeled uncertainties

• This paper considers the improvement of the control performance when the nonlinear system is affected by variations in their whole structure (kinematics and dynamics).
• This technique allows compensation for all model uncertainties by means of a single neural network and a sliding surface in a MIMO system.
• The proposed controllers are obtained by using the Lyapunov's stability theory.

This paper addresses the problem of adaptive neural sliding mode control for a class of multi-input multi-output nonlinear system. The control strategy is an inverse nonlinear controller combined with an adaptive neural network with sliding mode control using an on-line learning algorithm. The adaptive neural network with sliding mode control acts as a compensator for a conventional inverse controller in order to improve the control performance when the system is affected by variations in its entire structure (kinematics and dynamics). The controllers are obtained by using Lyapunov's stability theory. Experimental results of a case study show that the proposed method is effective in controlling dynamic systems with unexpected large uncertainties.