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Robotic Control Optimization Through Kernel Selection in Safe Bayesian Optimization by Zheng, Lihao; Wang, Hongxuan; Li, Xiaocong; Ma, Jun; Vadakkepat, Prahlad is a scholarly article available to read on EtoBox.

What is Robotic Control Optimization Through Kernel Selection in Safe Bayesian Optimization about?

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.

Author

Zheng, Lihao; Wang, Hongxuan; Li, Xiaocong; Ma, Jun; Vadakkepat, Prahlad

Published

2024

Language

EN