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

Abstract

The research objective of this paper is to propose a robust controller that relies only on input-output data information, in order to enforce robust tracking in a large class of uncertain nonlinear system. The controller is based on an adaptation approach, which is based on a fractional reaching law, while the control cmputation is directly proposed in discrete time, simplifying its digital implementation. The feedback gain is adapted through a fuzzy inference system that emulates a neural network, providing interesting capabilities to compensate for a large sort of uncertainties and un-modeled 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.