Efficient safe learning for controller tuning with experimental validation

TL;DR

This paper introduces a novel safe learning approach for controller tuning that replaces exhaustive grid search with optimization problems, leading to significant improvements in computational efficiency and tracking precision validated through simulations and industrial experiments.

Contribution

The paper proposes a new safe learning method that formulates optimization problems instead of grid search, enhancing efficiency and feasibility in controller tuning.

Findings

Achieves 30% better tracking accuracy.

Reduces computational cost by seven times.

Validated on industrial precision motion system.

Abstract

Optimization-based controller tuning is challenging because it requires formulating optimization problems explicitly as functions of controller parameters. Safe learning algorithms overcome the challenge by creating surrogate models from measured data. To ensure safety, such data-driven algorithms often rely on exhaustive grid search, which is computationally inefficient. In this paper, we propose a novel approach to safe learning by formulating a series of optimization problems instead of a grid search. We also develop a method for initializing the optimization problems to guarantee feasibility while using numerical solvers. The performance of the new method is first validated in a simulated precision motion system, demonstrating improved computational efficiency, and illustrating the role of exploiting numerical solvers to reach the desired precision. Experimental validation on an industrial-grade precision motion system confirms that the proposed algorithm achieves 30% better tracking at sub-micrometer precision as a state-of-the-art safe learning algorithm, improves the default auto-tuning solution, and reduces the computational cost seven times compared to learning algorithms based on exhaustive search.