Efficient sample selection for safe learning

TL;DR

This paper introduces an efficient reformulation of safe learning sample selection using optimization techniques, reducing computational costs and increasing flexibility in industrial control system safety assurance.

Contribution

It reformulates exhaustive search in safe learning as optimization problems, enabling the use of various solvers and introducing new stopping criteria for improved efficiency.

Findings

Reformulation allows using derivative-free optimization methods.

Enhanced flexibility in balancing solver accuracy and computational time.

Confirmed effectiveness through non-convex optimization and controller tuning applications.

Abstract

Ensuring safety in industrial control systems usually involves imposing constraints at the design stage of the control algorithm. Enforcing constraints is challenging if the underlying functional form is unknown. The challenge can be addressed by using surrogate models, such as Gaussian processes, which provide confidence intervals used to find solutions that can be considered safe. This in turn involves an exhaustive search on the entire search space. That approach can quickly become computationally expensive. We reformulate the exhaustive search as a series of optimization problems to find the next recommended points. We show that the proposed reformulation allows using a wide range of available optimization solvers, such as derivative-free methods. We show that by exploiting the properties of the solver, we enable the introduction of new stopping criteria into safe learning methods and increase flexibility in trading off solver accuracy and computational time. The results from a non-convex optimization problem and an application for controller tuning confirm the flexibility and the performance of the proposed reformulation.