Neural Adaptive Sliding Mode Control for a Class of Nonlinear Neutral Delay Systems

This paper is concerned with the problem of sliding mode control (SMC) for a class of neutral delay systems with unknown nonlinear uncertainties that may not satisfy the norm-bounded condition. A SMC scheme based on neural-network approximation is proposed for the uncertain neutral delay system. By means of linear matrix inequality (LMI) approach, a sufficient condition is given such that the resultant closed-loop system is guaranteed to be stable, and the states asymptotically converge to zero. When the LMI is feasible, the designs of both the sliding surface and the sliding mode control law can be easily obtained via convex optimization. It is shown that the state trajectories are driven toward the specified sliding surface that depends on the current states as well as the delayed states. Finally, a simulation result is given to illustrate the effectiveness of the proposed method.