Knowledge Commons of Institute of Automation,CAS
Neural-network-based online optimal control for uncertain non-linear continuous-time systems with control constraints | |
Yang, Xiong; Liu, Derong; Huang, Yuzhu | |
发表期刊 | IET CONTROL THEORY AND APPLICATIONS |
2013-11-21 | |
卷号 | 7期号:17页码:2037-2047 |
文章类型 | Article |
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. |
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 |
WOS标题词 | Science & Technology ; Technology |
关键词[WOS] | SATURATING ACTUATORS ; STABILIZATION ; STABILITY |
收录类别 | SCI |
语种 | 英语 |
WOS研究方向 | Automation & Control Systems ; Engineering ; Instruments & Instrumentation |
WOS类目 | Automation & Control Systems ; Engineering, Electrical & Electronic ; Instruments & Instrumentation |
WOS记录号 | WOS:000326109800001 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/3848 |
专题 | 多模态人工智能系统全国重点实验室_复杂系统智能机理与平行控制团队 |
作者单位 | Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
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. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Neural-network-based(493KB) | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | 浏览 |
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