Robotic Control Optimization Through Kernel Selection in Safe Bayesian Optimization
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909385984311296 |
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| author | Zheng, Lihao Wang, Hongxuan Li, Xiaocong Ma, Jun Vadakkepat, Prahlad |
| author_facet | Zheng, Lihao Wang, Hongxuan Li, Xiaocong Ma, Jun Vadakkepat, Prahlad |
| contents | Control system optimization has long been a fundamental challenge in robotics. While recent advancements have led to the development of control algorithms that leverage learning-based approaches, such as SafeOpt, to optimize single feedback controllers, scaling these methods to high-dimensional complex systems with multiple controllers remains an open problem. In this paper, we propose a novel learning-based control optimization method, which enhances the additive Gaussian process-based Safe Bayesian Optimization algorithm to efficiently tackle high-dimensional problems through kernel selection. We use PID controller optimization in drones as a representative example and test the method on Safe Control Gym, a benchmark designed for evaluating safe control techniques. We show that the proposed method provides a more efficient and optimal solution for high-dimensional control optimization problems, demonstrating significant improvements over existing techniques. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_07573 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Robotic Control Optimization Through Kernel Selection in Safe Bayesian Optimization Zheng, Lihao Wang, Hongxuan Li, Xiaocong Ma, Jun Vadakkepat, Prahlad Robotics Systems and Control Control system optimization has long been a fundamental challenge in robotics. While recent advancements have led to the development of control algorithms that leverage learning-based approaches, such as SafeOpt, to optimize single feedback controllers, scaling these methods to high-dimensional complex systems with multiple controllers remains an open problem. In this paper, we propose a novel learning-based control optimization method, which enhances the additive Gaussian process-based Safe Bayesian Optimization algorithm to efficiently tackle high-dimensional problems through kernel selection. We use PID controller optimization in drones as a representative example and test the method on Safe Control Gym, a benchmark designed for evaluating safe control techniques. We show that the proposed method provides a more efficient and optimal solution for high-dimensional control optimization problems, demonstrating significant improvements over existing techniques. |
| title | Robotic Control Optimization Through Kernel Selection in Safe Bayesian Optimization |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2411.07573 |