Design of Robust Adaptive Neural-Based Sliding-Mode Observer for Uncertain Nonlinear Systems

In this paper, a robust adaptive neural-based sliding-mode observer for achieving H∞tracking performance is proposed for a class of single-output nonlinear systems with unknown internal parameters and bounded external disturbances. The nonlinear system is first transformed by state-space change of coordinates into a special observable canonical form. Then, the adaptive neural networks and the sliding-mode control action are used for plant parameters estimation and to eliminate the effect of approximation error, respectively. Sufficient conditions are developed for achieving the H∞tracking performance in terms of linear matrix inequality (LMI) formulations. Our main contribution is nonlinear observers analysis and design methods that can effectively deal with model/plant mismatches. Finally, simulation results for a single-link robot are given to show the effectiveness of the proposed scheme.