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, ML
2022-01-01
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.File | Dimensione | Formato | |
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