Discrete-Time Adaptive Fractional Nonlinear Control Using Fuzzy Rules Emulating Networks

Abstract

The goal of this paper is to design a robust controller that relies only on input–output data to enforce robust tracking, when considering a large class of uncertain nonlinear system. The discrete-time controller is based on an adaptation approach that relies on a fractional reaching law. The feedback gain is adapted through a fuzzy inference system that emulates a neural network, providing interesting capabilities to compensate a large sort of uncertainties and unmodeled effects. The uniform ultimate boundedness of the tracking error is analyzed in the Lyapunov framework. Finally, an experimental assessment is studied to highlight the reliability of the proposed scheme.