Observer‐based neural adaptive fixed‐time tracking control for multi‐input multi‐output nonlinear systems with actuator faults

Summary This study developed a new strategy for adaptive fixed‐time output feedback control of multi‐input multi‐output (MIMO) nonlinear systems involving unmeasurable external disturbances and actuator faults. Two actuator faults, loss of effectiveness and lock‐in‐place were simultaneously consider...

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Published inInternational journal of robust and nonlinear control Vol. 33; no. 12; pp. 6849 - 6872
Main Authors Song, Xiaona, Sun, Peng, Ahn, Choon Ki, Song, Shuai
Format Journal Article
LanguageEnglish
Published Bognor Regis Wiley Subscription Services, Inc 01.08.2023
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ISSN1049-8923
1099-1239
DOI10.1002/rnc.6727

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Summary:Summary This study developed a new strategy for adaptive fixed‐time output feedback control of multi‐input multi‐output (MIMO) nonlinear systems involving unmeasurable external disturbances and actuator faults. Two actuator faults, loss of effectiveness and lock‐in‐place were simultaneously considered. Furthermore, a composite observer was used in MIMO systems to estimate unmeasurable states and external disturbances simultaneously, and radial basis function neural networks were employed to identify unknown internal nonlinearities. Additionally, the command‐filtered backstepping approach was applied to avoid tedious analytic calculations of the backstepping framework, and a compensation item was constructed to attenuate the filter error. A fault‐tolerant controller was developed to ensure that the overall states of the controlled system were practically fixed‐time bounded while the tracking error was regulated to the equilibrium region having a fixed‐time convergence ratio. Illustrative studies showed the validity and practicability of the proposed theory.
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ISSN:1049-8923
1099-1239
DOI:10.1002/rnc.6727