Adaptive finite‐time prescribed performance tracking control for unknown nonlinear systems subject to full‐state constraints and input saturation

In this article, an adaptive finite‐time prescribed performance tracking control scheme is developed for a class of strict‐feedback unknown nonlinear systems with both full‐state constrained and input saturation. To deal with the full state constraint, a distinctive method of employing a barrier fun...

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Bibliographic Details
Published inInternational journal of robust and nonlinear control Vol. 32; no. 17; pp. 9347 - 9362
Main Authors Chang, Ru, Bai, Zhi‐Zhong, Zhang, Bo‐Yuan, Sun, Chang‐Yin
Format Journal Article
LanguageEnglish
Published Bognor Regis Wiley Subscription Services, Inc 25.11.2022
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Summary:In this article, an adaptive finite‐time prescribed performance tracking control scheme is developed for a class of strict‐feedback unknown nonlinear systems with both full‐state constrained and input saturation. To deal with the full state constraint, a distinctive method of employing a barrier function based transformation is used rather than the barrier Lyapunov function based method, and thus the undesirable “feasibility conditions” are completely eliminated. To overcome the problem of input saturation nonlinearity, the smooth nonaffine function is adopted to approximate the input saturation function. Then, with the aid of a new nonlinear mapping technique, a low‐complexity adaptive finite‐time prescribed performance tracking controller is designed by the dynamic surface control based backstepping method, which can guarantee that the tracking error can converge to a small fixed region at settling time with fast convergence rate and always stays within the region later, simultaneously, all the signals in the closed‐loop system are bounded. Finally, simulation results show the effectiveness of the proposed control scheme.
Bibliography:Funding information
China Postdoctoral Science Foundation, Grant/Award Number: 2020M681449; Natural Science Foundation of Shanxi Province, Grant/Award Number: 201901D211161
ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1049-8923
1099-1239
DOI:10.1002/rnc.6358