Safe Bayesian Optimization for High-Dimensional Control Systems via Additive Gaussian Processes
<|reference_start|>Safe Bayesian Optimization for High-Dimensional Control Systems via Additive Gaussian Processes: Controller tuning and optimization have been among the most fundamental problems in robotics and mechatronic systems. The traditional methodology is usually model-based, but its performance heavily relies on an accurate mathematical model of the system. In control applications with complex dynamics, obtaining a precise model is often challenging, leading us towards a data-driven approach. While optimizing a single controller has been explored by various researchers, it remains a challenge to obtain the optimal controller parameters safely and efficiently when multiple controllers are involved. In this paper, we propose a high-dimensional safe Bayesian optimization method based on additive Gaussian processes to optimize multiple controllers simultaneously and safely. Additive Gaussian kernels replace the traditional squared-exponential kernels or Mat\'ern kernels, enhancing the efficiency with which Gaussian processes update information on unknown functions. Experimental results on a permanent magnet synchronous motor (PMSM) demonstrate that compared to existing safe Bayesian optimization algorithms, our method can obtain optimal parameters more efficiently while ensuring safety.<|reference_end|>
@article{wang2024safe,
    title={Safe Bayesian Optimization for High-Dimensional Control Systems via
  Additive Gaussian Processes},
    author={Hongxuan Wang, Xiaocong Li, Adrish Bhaumik and Prahlad Vadakkepat},
    journal={arXiv preprint arXiv:2408.16307},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.16307},
    primaryClass={cs.RO cs.AI}
}