控制理论(社会学)
计算机科学
稳健性(进化)
移动机器人
鲁棒控制
维数之咒
操作员(生物学)
滑模控制
控制器(灌溉)
控制工程
机器人
非线性系统
控制系统
人工智能
控制(管理)
工程类
转录因子
生物
农学
基因
量子力学
电气工程
物理
生物化学
抑制因子
化学
作者
Chao Ren,Hongjian Jiang,Chunli Li,Weichao Sun,Shugen Ma
出处
期刊:IEEE-ASME Transactions on Mechatronics
[Institute of Electrical and Electronics Engineers]
日期:2023-02-01
卷期号:28 (1): 461-472
标识
DOI:10.1109/tmech.2022.3203518
摘要
The Koopman operator theory offers a way to construct explicit control-oriented high-dimensional linear dynamical models for the original nonlinear systems, solely using the input–output data of the dynamical system. The modeling accuracy of the Koopman model largely depends on the basis functions (lifting functions), dimensionality, and data quality. However, there has not been a systematic way to solve the problems mentioned above. In this article, a Koopman-operator-based robust data-driven control framework is proposed for wheeled mobile robots, via incorporating tools from control theory, to solve the problem of modeling errors of the Koopman model. By employing an extended state observer, the modeling errors of the Koopman model, including unknown external disturbances, are online estimated and compensated in the control signal in real time. Then, sliding-mode control is used to synthesize the controller. Importantly, the method of virtual control input is proposed, to cope with the model errors arising from the rotational motion of all the mobile robots. Besides, stability analysis is conducted, and the optimal dimensionality of the Koopman model is experimentally selected. Finally, experimental tests on an omnidirectional mobile robot are carried out to verify the effectiveness of the proposed control scheme, in terms of tracking performance and robustness.
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