Autogeneration of Mission-Oriented Robot Controllers Using Bayesian-Based Koopman Operator
文献类型：期刊论文
| 作者 | Pan, Jie1; Li, Dongyue1; Wang, Jian2,3; Zhang, Pengfei2,3 ; Shao, Jinyan4; Yu, Junzhi1
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| 刊名 | IEEE TRANSACTIONS ON ROBOTICS
 |
| 出版日期 | 2024 |
| 卷号 | 40页码:903-918 |
| ISSN号 | 1552-3098 |
| 关键词 | Bayesian optimization (BO) control-oriented model identification Koopman operator robot control |
| DOI | 10.1109/TRO.2023.3344033 |
| 通讯作者 | Yu, Junzhi(junzhi.yu@ia.ac.cn) |
| 英文摘要 | Model-based robot controllers require customized control-oriented models, involving expert knowledge and trial and error. Remarkably, the Koopman operator enables the control-oriented model identification through the input-output mapping set, breaking through the barriers of the customization services. However, in recent years, research on Koopman-based robot control has mostly focused on lifting function construction, deviating from the original intention of improving the controller performance. Thus, we propose a robot controller autogeneration framework using the Bayesian-based Koopman operator, significantly releasing labor and eliminating the design obstacle. First, we introduce the Koopman-based system identification method and offer the basic lifting function design criteria. Then, a Bayesian-based optimization strategy with resource allocation is designed, which allows for the simultaneous optimization of the lifting function and the controller. Next, taking model-predictive control (MPC) as an example, a mission-oriented controller autogeneration framework is developed. Simulation and experimental results indicate that, under various robots and data sources, the proposed framework can effectively generate the robot controllers and perform with a far greater level of mission accuracy than the unoptimized Koopman-based MPC. Meanwhile, the proposed technique exhibits an obvious compensation effect against disturbances, demonstrating its practicability in robot control. |
| WOS关键词 | TRAJECTORY TRACKING CONTROL ; AUTONOMOUS VEHICLE ; OPTIMIZATION ; DESIGN ; LOOP |
| 资助项目 | National Natural Science Foundation of China |
| WOS研究方向 | Robotics |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:001141658900001 |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/54832]  |
| 专题 | 复杂系统认知与决策实验室 |
| 通讯作者 | Yu, Junzhi |
| 作者单位 | 1.Peking Univ, Coll Engn, Dept Adv Mfg & Robot, State Key Lab Turbulence & Complex Syst, Beijing 100871, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 4.Peking Univ, Nanchang Innovat Inst, Nanchang 330096, Peoples R China |
| 推荐引用方式 GB/T 7714 | Pan, Jie,Li, Dongyue,Wang, Jian,et al. Autogeneration of Mission-Oriented Robot Controllers Using Bayesian-Based Koopman Operator[J]. IEEE TRANSACTIONS ON ROBOTICS,2024,40:903-918. |
| APA | Pan, Jie,Li, Dongyue,Wang, Jian,Zhang, Pengfei,Shao, Jinyan,&Yu, Junzhi.(2024).Autogeneration of Mission-Oriented Robot Controllers Using Bayesian-Based Koopman Operator.IEEE TRANSACTIONS ON ROBOTICS,40,903-918. |
| MLA | Pan, Jie,et al."Autogeneration of Mission-Oriented Robot Controllers Using Bayesian-Based Koopman Operator".IEEE TRANSACTIONS ON ROBOTICS 40(2024):903-918. |
入库方式： OAI收割
来源：自动化研究所
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