Compound FAT-Based Learning Control of Uncertain Fractional-Order Nonlinear Systems With Disturbance

Abstract

This letter studies a function approximation technique (FAT)-based fractional-order (FO) backstepping compound learning control for uncertain FO strict-feedback nonlinear systems with unknown external disturbance. A FAT-based learning is used to approximate unknown dynamic terms. The proposed control algorithm takes into account the accuracy of FAT approximation by defining a prediction error obtained from a FO serial-parallel identifier (FOSPI). Furthermore, the FO command-filtered approach is adopted to reduce the complexity explosion of the backstepping-based design. New FO compound adaptation laws are constructed by integrating effective feedbacks derived from compensated tracking error and the accuracy of FAT learning. The stability of the overall system is analyzed by the Lyapunov stability concept. Simulations validate the theoretical results.