Autogeneration of Mission-Oriented Robot Controllers Using Bayesian-Based Koopman Operator

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 servic...

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Bibliographic Details
Published in:IEEE transactions on robotics 2024, Vol.40, p.903-918
Main Authors: Pan, Jie, Li, Dongyue, Wang, Jian, Zhang, Pengfei, Shao, Jinyan, Yu, Junzhi
Format: Article
Language:English
Subjects:
Adaptation models
Bayes methods
Bayesian analysis
Bayesian optimization (BO)
control-oriented model identification
Controllers
Customization
Design criteria
Design optimization
Hoisting
Identification methods
Koopman operator
Mathematical models
Optimization
Predictive control
Resource allocation
Robot control
Robots
System identification
Vehicle dynamics
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Summary: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.
ISSN:1552-3098
1941-0468
DOI:10.1109/TRO.2023.3344033