Adaptive neural control for uncertain constrained pure feedback systems with severe sensor faults: a complexity reduced approach
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 bac...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/163724 |
| _version_ | 1826123239400669184 |
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| author | Huang, Xiucai Wen, Changyun Song, Yongduan |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Huang, Xiucai Wen, Changyun Song, Yongduan |
| author_sort | Huang, Xiucai |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T06:01:24Z |
| format | Journal Article |
| id | ntu-10356/163724 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:01:24Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | ntu-10356/1637242022-12-15T02:54:38Z Adaptive neural control for uncertain constrained pure feedback systems with severe sensor faults: a complexity reduced approach Huang, Xiucai Wen, Changyun Song, Yongduan School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Adaptive Neural Control Sensor Faults 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. This research was supported by the National Key Research and Development Program of China under Grant (No. 2021ZD0201300), the National Natural Science Foundation of China (No. 61991400, 61991403, 61860206008, 61933012), and in part by the Fundamental Research Funds for the Central Universities under Project (No. 2021CDJXKJC001), by the Science and Technology Research Programof Chongqing Municipal Education Commission (No. KJZDM202100101) and by Chongqing Human Resources and Social Security Bureau (No. cx2021114). 2022-12-15T02:54:38Z 2022-12-15T02:54:38Z 2023 Journal Article 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 0005-1098 https://hdl.handle.net/10356/163724 10.1016/j.automatica.2022.110701 2-s2.0-85141656528 147 110701 en Automatica © 2022 Elsevier Ltd. All rights reserved. |
| spellingShingle | Engineering::Electrical and electronic engineering Adaptive Neural Control Sensor Faults Huang, Xiucai Wen, Changyun Song, Yongduan Adaptive neural control for uncertain constrained pure feedback systems with severe sensor faults: a complexity reduced approach |
| title | Adaptive neural control for uncertain constrained pure feedback systems with severe sensor faults: a complexity reduced approach |
| title_full | Adaptive neural control for uncertain constrained pure feedback systems with severe sensor faults: a complexity reduced approach |
| title_fullStr | Adaptive neural control for uncertain constrained pure feedback systems with severe sensor faults: a complexity reduced approach |
| title_full_unstemmed | Adaptive neural control for uncertain constrained pure feedback systems with severe sensor faults: a complexity reduced approach |
| title_short | Adaptive neural control for uncertain constrained pure feedback systems with severe sensor faults: a complexity reduced approach |
| title_sort | adaptive neural control for uncertain constrained pure feedback systems with severe sensor faults a complexity reduced approach |
| topic | Engineering::Electrical and electronic engineering Adaptive Neural Control Sensor Faults |
| url | https://hdl.handle.net/10356/163724 |
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