In this paper, a novel data-driven control algorithm is presented coupling Model-Free Adaptive Control and Sliding Mode Control, which addresses general discrete-time Single-Input Single-Output nonlinear nonaffine systems and is aimed at strengthening standard techniques in the presence of a class of output-dependent perturbations. Use is made of an equivalent dynamic linearization model obtained adopting a dynamic linearization technique based on pseudo-partial derivatives. A stability proof of convergence of the closed loop system is provided, showing that the closed-loop tracking error is an asymptotically vanishing sequence and ensuring boundedness of the I/O sequences. Validation of the technique has been performed using a discrete-time test plant taken from the literature in the presence of perturbations. Simulation results show a remarkable improvement in terms of control authority and of tracking accuracy with respect to recently published analogous approaches.

A Robust Sliding-Mode based Data-Driven Model-Free Adaptive Controller

Corradini M. L.
Primo
2021-01-01

Abstract

In this paper, a novel data-driven control algorithm is presented coupling Model-Free Adaptive Control and Sliding Mode Control, which addresses general discrete-time Single-Input Single-Output nonlinear nonaffine systems and is aimed at strengthening standard techniques in the presence of a class of output-dependent perturbations. Use is made of an equivalent dynamic linearization model obtained adopting a dynamic linearization technique based on pseudo-partial derivatives. A stability proof of convergence of the closed loop system is provided, showing that the closed-loop tracking error is an asymptotically vanishing sequence and ensuring boundedness of the I/O sequences. Validation of the technique has been performed using a discrete-time test plant taken from the literature in the presence of perturbations. Simulation results show a remarkable improvement in terms of control authority and of tracking accuracy with respect to recently published analogous approaches.
2021
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11581/452215
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