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Research Article | Open Access

Observer-based robust preview tracking control for a class of continuous-time Lipschitz nonlinear systems

Xiao Yu1( )Yan Hua1Yanrong Lu2
School of Science, Shandong Jianzhu University, Jinan 250101, China
College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China
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Abstract

In this paper, a novel observer-based robust preview tracking controller design method is proposed for a class of continuous-time Lipschitz nonlinear systems with external disturbances and unknown states. First, a state observer is designed to reconstruct unknown system states. Second, using differentiation, the state lifting technique, the differential mean value theorem, and several ingenious mathematical manipulations, an augmented error system (AES) containing the previewable information of a reference signal is constructed, thereby transforming the tracking control problem into a robust H control problem. Based on linear parameter-varying (LPV) system theory, a sufficient condition for asymptotic stability of a closed-loop system with a robust H performance level is established in terms of the linear matrix inequality (LMI). Furthermore, a tracking controller, which includes observer-based feedback control, integral control, and preview feedforward compensation, is established for the original system. In particular, the tracking controller design is simplified by computing the observer and tracking controller gains simultaneously via only a one-step LMI algorithm. Finally, numerical simulation results demonstrate that the proposed controller leads to superior improvement in the output tracking performance compared with the existing methods.

CLC number: 34H05, 93B52, 93C10

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AIMS Mathematics
Pages 26741-26764

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Cite this article:
Yu X, Hua Y, Lu Y. Observer-based robust preview tracking control for a class of continuous-time Lipschitz nonlinear systems. AIMS Mathematics, 2024, 9(10): 26741-26764. https://doi.org/10.3934/math.20241301

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Received: 06 June 2024
Revised: 07 August 2024
Accepted: 26 August 2024
Published: 15 October 2024
©2024 the Author(s), licensee AIMS Press.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0)