Model-free Optimal Control based Intelligent Cruise Control with Hardware-in-the-loop Demonstration
文献类型：期刊论文
| 作者 | Zhao, Dongbin1,2,3 ; Xia, Zhongpu4; Zhang, Qichao1,2
|
| 刊名 | IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE
 |
| 出版日期 | 2017-05-01 |
| 卷号 | 12期号:2页码:56-69 |
| 关键词 | Intelligent Cruise Control |
| DOI | 10.1109/MCI.2017.2670463 |
| 文献子类 | Article |
| 英文摘要 | It is difficult to implement optimal control for a system whose model is unknown and operation environment is uncertain, such as the intelligent cruise control of vehicles. This article will address the problem from the perspective of reinforcement learning by learning the optimal policy from the state transition data. The model-free optimal control algorithm is employed to approximate the optimal control policy for the intelligent cruise control system, which considers the comfort performance and the safety performance comprehensively by setting up a total performance index. The algorithm is implemented by two multi-layer neural networks which are the critic network and the actor network. The critic and actor networks are employed to approximate the state-action value function and the control action, respectively. In addition, a data collecting strategy is proposed to obtain the state transition data distributed uniformly in the state action space from the running trajectory of the host car. The critic network and the action network are trained alternatively by the collected data until converging. The convergent action network is used to obtain the optimal control policy. At last, the policy is tested on a hardware-in-the-loop simulator built upon dSPACE by comparing with a linear quadratic regulator (LQR) controller and a proportion integration differentiation (PID) controller. Results show its excellent performance on both aspects of the safety and the comfort. |
| WOS关键词 | LONGITUDINAL CONTROL ; POLICY ITERATION ; CONTROL DESIGN ; AVOIDANCE ; VEHICLES ; SYSTEM |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000399714900005 |
| 资助机构 | National Natural Science Foundation of China (NSFC)(61573353 ; National Key Research and Development Plan(2016YFB0101000) ; 61533017) |
| 源URL | [http://ir.ia.ac.cn/handle/173211/14337]  |
| 专题 | 复杂系统管理与控制国家重点实验室_深度强化学习 |
| 通讯作者 | Zhang, Qichao |
| 作者单位 | 1.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China 3.Jiangsu Huimin Traff Facil Co Ltd, Huaian, Jiangsu, Peoples R China 4.Baidu Com Times Technol Co Ltd, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhao, Dongbin,Xia, Zhongpu,Zhang, Qichao. Model-free Optimal Control based Intelligent Cruise Control with Hardware-in-the-loop Demonstration[J]. IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE,2017,12(2):56-69. |
| APA | Zhao, Dongbin,Xia, Zhongpu,&Zhang, Qichao.(2017).Model-free Optimal Control based Intelligent Cruise Control with Hardware-in-the-loop Demonstration.IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE,12(2),56-69. |
| MLA | Zhao, Dongbin,et al."Model-free Optimal Control based Intelligent Cruise Control with Hardware-in-the-loop Demonstration".IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE 12.2(2017):56-69. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。