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Adaptive decentralized prescribed performance control for a class of large-scale nonlinear systems subject to nonsymmetric input saturations

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Abstract

This paper investigates an adaptive decentralized predefined performance control problem for a class of large-scale nonlinear systems with nonsymmetric input saturation by using multi-dimensional taylor network (MTN) approach. Firstly, the input saturation model is approximated by a smooth function with a bounded approximation error and unknown nonlinear functions are estimated by MTNs. Secondly, a decentralized tracking control algorithm is established by integrating the idea of prescribed performance control into backstepping recursive technique. Thirdly, by using the designed MTN-based adaptive decentralized controller, all the closed-loop signals are bounded and all the tracking errors satisfy the predefined transient and steady-state performance, respectively. Finally, the presented control method is effective by introducing three examples, and the simulation results verify that the correctness and reasonableness of the proposed control algorithm.

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Funding

This work was supported by the Shandong Provincial Natural Science Foundation, China (No. ZR2020QF055).

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  1. School of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao, 266061, China

    Shan-Liang Zhu & Yu-Qun Han

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  1. Shan-Liang Zhu
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Zhu, SL., Han, YQ. Adaptive decentralized prescribed performance control for a class of large-scale nonlinear systems subject to nonsymmetric input saturations. Neural Comput & Applic 34, 11123–11140 (2022). https://doi.org/10.1007/s00521-022-07032-8

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