Fault-tolerant control for nonlinear systems with a dead zone: Reinforcement learning approach
This paper focuses on the adaptive reinforcement learning-based optimal control problem for standard nonstrict-feedback nonlinear systems with the actuator fault and an unknown dead zone. To simultaneously reduce the computational complexity and eliminate the local optimal problem, a novel neural ne...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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AIMS Press
2023-02-01
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| Series: | Mathematical Biosciences and Engineering |
| Subjects: | |
| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2023274?viewType=HTML |
| _version_ | 1828010377757065216 |
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| author | Zichen Wang Xin Wang |
| author_facet | Zichen Wang Xin Wang |
| author_sort | Zichen Wang |
| collection | DOAJ |
| description | This paper focuses on the adaptive reinforcement learning-based optimal control problem for standard nonstrict-feedback nonlinear systems with the actuator fault and an unknown dead zone. To simultaneously reduce the computational complexity and eliminate the local optimal problem, a novel neural network weight updated algorithm is presented to replace the classic gradient descent method. By utilizing the backstepping technique, the actor critic-based reinforcement learning control strategy is developed for high-order nonlinear nonstrict-feedback systems. In addition, two auxiliary parameters are presented to deal with the input dead zone and actuator fault respectively. All signals in the system are proven to be semi-globally uniformly ultimately bounded by Lyapunov theory analysis. At the end of the paper, some simulation results are shown to illustrate the remarkable effect of the proposed approach. |
| first_indexed | 2024-04-10T08:53:49Z |
| format | Article |
| id | doaj.art-c8b9603e697c4beaad19008d6eabeb9d |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-04-10T08:53:49Z |
| publishDate | 2023-02-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-c8b9603e697c4beaad19008d6eabeb9d2023-02-22T01:19:33ZengAIMS PressMathematical Biosciences and Engineering1551-00182023-02-012046334635710.3934/mbe.2023274Fault-tolerant control for nonlinear systems with a dead zone: Reinforcement learning approachZichen Wang0Xin Wang11. College of Westa, Southwest University, Chongqing 400715, China2. College of Electronic and Information Engineering, Southwest University, Chongqing 400715, ChinaThis paper focuses on the adaptive reinforcement learning-based optimal control problem for standard nonstrict-feedback nonlinear systems with the actuator fault and an unknown dead zone. To simultaneously reduce the computational complexity and eliminate the local optimal problem, a novel neural network weight updated algorithm is presented to replace the classic gradient descent method. By utilizing the backstepping technique, the actor critic-based reinforcement learning control strategy is developed for high-order nonlinear nonstrict-feedback systems. In addition, two auxiliary parameters are presented to deal with the input dead zone and actuator fault respectively. All signals in the system are proven to be semi-globally uniformly ultimately bounded by Lyapunov theory analysis. At the end of the paper, some simulation results are shown to illustrate the remarkable effect of the proposed approach.https://www.aimspress.com/article/doi/10.3934/mbe.2023274?viewType=HTMLfault-tolerant controlinput dead zonenonstrict-feedbacknonlinear systemreinforcement learning |
| spellingShingle | Zichen Wang Xin Wang Fault-tolerant control for nonlinear systems with a dead zone: Reinforcement learning approach Mathematical Biosciences and Engineering fault-tolerant control input dead zone nonstrict-feedback nonlinear system reinforcement learning |
| title | Fault-tolerant control for nonlinear systems with a dead zone: Reinforcement learning approach |
| title_full | Fault-tolerant control for nonlinear systems with a dead zone: Reinforcement learning approach |
| title_fullStr | Fault-tolerant control for nonlinear systems with a dead zone: Reinforcement learning approach |
| title_full_unstemmed | Fault-tolerant control for nonlinear systems with a dead zone: Reinforcement learning approach |
| title_short | Fault-tolerant control for nonlinear systems with a dead zone: Reinforcement learning approach |
| title_sort | fault tolerant control for nonlinear systems with a dead zone reinforcement learning approach |
| topic | fault-tolerant control input dead zone nonstrict-feedback nonlinear system reinforcement learning |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2023274?viewType=HTML |
| work_keys_str_mv | AT zichenwang faulttolerantcontrolfornonlinearsystemswithadeadzonereinforcementlearningapproach AT xinwang faulttolerantcontrolfornonlinearsystemswithadeadzonereinforcementlearningapproach |