Fractional-Order Multivariable Adaptive Control Based on a Nonlinear Scalar Update Law

Abstract

This paper proposes a new fractional-order model reference adaptive control (FOMRAC) framework for a fractional-order multivariable system with parameter uncertainty. The designed FOMRAC scheme depends on a fractional-order nonlinear scalar update law. Specifically, the scalar update law does not change as the input–output dimension changes. The main advantage of the proposed adaptive controller is that only one parameter online update is needed such that the computational burden in the existing FOMRAC can be relieved. Furthermore, we show that all signals in this adaptive scheme are bounded and the mean value of the squared norm of the error converges to zero. Two illustrative numerical examples are presented to demonstrate the efficiency of the proposed control scheme.

Research on the Control Problem of Autonomous Underwater Vehicles Based on Strongly Coupled Radial Basis Function Conditions

“…The research object of adaptive control is a system whose parameters are not entirely determined, and the environment contains unknown and random factors. However, adaptive control is more complicated and costly than conventional control [14][15][16][17]. Model predictive control (MPC) is an optimization-based time domain control method [18].…”