Wang, H., Li, X., Zheng, L., Bhaumik, A., & Vadakkepat, P. (2025). Safe Bayesian Optimization for Complex Control Systems via Additive Gaussian Processes. IEEE Robotics and Automation Letters, 10(11), 11538–11545. https://doi.org/10.1109/lra.2025.3612756
Abstract:
Controller tuning and optimization have long been
recognized as fundamental challenges in robotics and mechatronic
systems. Traditional controller design techniques are
usually model-based, and their closed-loop performance depends
on the fidelity of the mathematical model. Subsequent tuning
of the controller parameters is frequently carried out via empirical
rules, which may still suffer from model inaccuracies.
In control applications with complex dynamics, obtaining a
precise model is often challenging, leading us towards a datadriven
approach. While various researchers have explored the
optimization of a single controller, it remains a challenge to
obtain the optimal controller parameters safely and efficiently
when multiple controllers are involved. In this letter, a method
called SAFECTRLBO is proposed to optimize multiple controllers
simultaneously while ensuring safety. The exploration process
in existing safe Bayesian optimization is simplified to reduce
computational effort without sacrificing expansion capability.
Additionally, additive Gaussian kernels are employed to enhance
the efficiency of Gaussian process updates for unknown functions.
Hardware experiments on a permanent magnet synchronous
motor (PMSM) demonstrate that, compared to baseline safe
Bayesian optimization algorithms, SAFECTRLBO attains the best
overall performance while ensuring safety.
License type:
Publisher Copyright
Funding Info:
There was no specific funding for the research done