Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/163724
Title: Adaptive neural control for uncertain constrained pure feedback systems with severe sensor faults: a complexity reduced approach
Authors: Huang, Xiucai
Wen, Changyun
Song, Yongduan
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2023
Source: Huang, X., Wen, C. & Song, Y. (2023). Adaptive neural control for uncertain constrained pure feedback systems with severe sensor faults: a complexity reduced approach. Automatica, 147, 110701-. https://dx.doi.org/10.1016/j.automatica.2022.110701
Journal: Automatica
Abstract: This paper investigates the output tracking control problem for multi-input multi-output (MIMO) uncertain pure-feedback systems subject to time-varying asymmetric output constraints, where the states are polluted by multiplicative/additive faults. By incorporating some auxiliary functions into a backstepping-like design procedure, a smooth adaptive control scheme is constructed using neural network (NN) approximation, making the closed-loop dynamics exhibits a unique feasible solution with all the involved signals evolving within some compact sets during a finite time interval. As a result, the safety and reliability of the application of NN approximators is guaranteed in advance and the algebraic loop issue arising from the control input coupling is removed completely. Thereafter, by combining the Lyapunov stability analysis with contradiction, the boundedness of those signals over the entire time domain is established. It is shown that with the proposed control scheme, the impact of the sensor faults from all state (except for output) on the output tracking is counteracted automatically while maintaining the output constraints. Furthermore, the proposed method enlarges the pure feedback systems considered by relaxing the state-of-the-art controllability conditions. Finally, the efficacy of the approach is verified and clarified via simulation studies.
URI: https://hdl.handle.net/10356/163724
ISSN: 0005-1098
DOI: 10.1016/j.automatica.2022.110701
Schools: School of Electrical and Electronic Engineering 
Rights: © 2022 Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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