Output-feedback control design for switched nonlinear systems: Adaptive neural backstepping approach

Abstract

The tracking control is addressed for switched nonlinear systems with arbitrary switchings by adaptive neural approach via output feedback. The switched systems under consideration are composed of some subsystems in non-strict -feedback form. It is assumed that all the system state variables are unavailable, but the output variable is measurable. A switched nonlinear observer is set up to estimate those unmeasurable state variables. Convex combination method is utilized to determine the observer gain matrix so that the effect from those nonlinear terms can be well compensated for. By the feature of the basis vector functions of radial basis neural networks (RBF NN), a new observer-based adaptive neural backstepping control design scheme is presented for the switched nonlinear non-strict feedback systems. The multiple Lyapunov method is used to show the stability of the adaptive closed-loop systems. It is also proven that the tracking error converges to a small neighborhood around the original point under the action of the suggested controllers. Finally, a simulation example is studied to test the efficacy of the suggested control strategies.