Neural-network-based online optimal control for uncertain non-linear continuous-time systems with control constraints
文献类型：期刊论文
| 作者 | Yang, Xiong ; Liu, Derong; Huang, Yuzhu
|
| 刊名 | IET CONTROL THEORY AND APPLICATIONS
 |
| 出版日期 | 2013-11-21 |
| 卷号 | 7期号:17页码:2037-2047 |
| 关键词 | adaptive control approximation theory closed loop systems continuous time systems Lyapunov methods neurocontrollers nonlinear control systems optimal control robust control uncertain systems neural network-based online adaptive optimal control uncertain nonlinear continuous-time systems control constraints infinite-horizon optimal control problem control policy saturation constraints identifier-critic architecture Hamilton-Jacobi-Bellman equation approximation uncertain system dynamics critic NN action-critic dual networks reinforcement learning identifier NN policy iteration LyapunovaEuros direct method closed loop system stability |
| 英文摘要 | In this study, an online adaptive optimal control scheme is developed for solving the infinite-horizon optimal control problem of uncertain non-linear continuous-time systems with the control policy having saturation constraints. A novel identifier-critic architecture is presented to approximate the Hamilton-Jacobi-Bellman equation using two neural networks (NNs): an identifier NN is used to estimate the uncertain system dynamics and a critic NN is utilised to derive the optimal control instead of typical action-critic dual networks employed in reinforcement learning. Based on the developed architecture, the identifier NN and the critic NN are tuned simultaneously. Meanwhile, unlike initial stabilising control indispensable in policy iteration, there is no special requirement imposed on the initial control. Moreover, by using Lyapunov's direct method, the weights of the identifier NN and the critic NN are guaranteed to be uniformly ultimately bounded, while keeping the closed-loop system stable. Finally, an example is provided to demonstrate the effectiveness of the present approach. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Automation & Control Systems ; Engineering, Electrical & Electronic ; Instruments & Instrumentation |
| 研究领域[WOS] | Automation & Control Systems ; Engineering ; Instruments & Instrumentation |
| 关键词[WOS] | SATURATING ACTUATORS ; STABILIZATION ; STABILITY |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000326109800001 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/3848]  |
| 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 |
| 作者单位 | Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yang, Xiong,Liu, Derong,Huang, Yuzhu. Neural-network-based online optimal control for uncertain non-linear continuous-time systems with control constraints[J]. IET CONTROL THEORY AND APPLICATIONS,2013,7(17):2037-2047. |
| APA | Yang, Xiong,Liu, Derong,&Huang, Yuzhu.(2013).Neural-network-based online optimal control for uncertain non-linear continuous-time systems with control constraints.IET CONTROL THEORY AND APPLICATIONS,7(17),2037-2047. |
| MLA | Yang, Xiong,et al."Neural-network-based online optimal control for uncertain non-linear continuous-time systems with control constraints".IET CONTROL THEORY AND APPLICATIONS 7.17(2013):2037-2047. |
入库方式： OAI收割
来源：自动化研究所
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