Synergetic Learning Neuro-Control for Unknown Affine Nonlinear Systems With Asymptotic Stability Guarantees
文献类型：期刊论文
| 作者 | Zhu, Liao1,2 ; Wei, Qinglai3,4,5; Guo, Ping1,2
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| 刊名 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
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| 出版日期 | 2024-01-08 |
| 页码 | 11 |
| 关键词 | Approximate dynamic programming (ADP) neural network off-policy optimal control reinforcement learning (RL) |
| ISSN号 | 2162-237X |
| DOI | 10.1109/TNNLS.2023.3347663 |
| 通讯作者 | Guo, Ping(pguo@bnu.edu.cn) |
| 英文摘要 | For completely unknown affine nonlinear systems, in this article, a synergetic learning algorithm (SLA) is deve-loped to learn an optimal control. Unlike the conventional Hamilton-Jacobi-Bellman equation (HJBE) with system dynamics, a model-free HJBE (MF-HJBE) is deduced by means of off-policy reinforcement learning (RL). Specifically, the equivalence between HJBE and MF-HJBE is first bridged from the perspective of the uniqueness of the solution of the HJBE. Furthermore, it is proven that once the solution of MF-HJBE exists, its corresponding control input renders the system asymptotically stable and optimizes the cost function. To solve the MF-HJBE, the two agents composing the synergetic learning (SL) system, the critic agent and the actor agent, can evolve in real-time using only the system state data. By building an experience reply (ER)-based learning rule, it is proven that when the critic agent evolves toward the optimal cost function, the actor agent not only evolves toward the optimal control, but also guarantees the asymptotic stability of the system. Finally, simulations of the F16 aircraft system and the Van der Pol oscillator are conducted and the results support the feasibility of the developed SLA. |
| WOS关键词 | ADAPTIVE OPTIMAL-CONTROL ; EXPERIENCE REPLAY ; APPROXIMATION ; ALGORITHM ; ITERATION |
| 资助项目 | National Key Research and Development Program of China |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001167322100001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Key Research and Development Program of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/57818]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_智能化团队 |
| 通讯作者 | Guo, Ping |
| 作者单位 | 1.Beijing Normal Univ, Int Acad Ctr Complex Syst, Zhuhai 519087, Peoples R China 2.Beijing Normal Univ, Sch Syst Sci, Beijing 100875, Peoples R China 3.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence, Beijing 100190, Peoples R China 4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 5.Macau Univ Sci & Technol, Inst Syst Engn, Taipa, Macao, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhu, Liao,Wei, Qinglai,Guo, Ping. Synergetic Learning Neuro-Control for Unknown Affine Nonlinear Systems With Asymptotic Stability Guarantees[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2024:11. |
| APA | Zhu, Liao,Wei, Qinglai,&Guo, Ping.(2024).Synergetic Learning Neuro-Control for Unknown Affine Nonlinear Systems With Asymptotic Stability Guarantees.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,11. |
| MLA | Zhu, Liao,et al."Synergetic Learning Neuro-Control for Unknown Affine Nonlinear Systems With Asymptotic Stability Guarantees".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2024):11. |
入库方式： OAI收割
来源：自动化研究所
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