CONFIG: Constrained Efficient Global Optimization for Closed-Loop Control System Optimization with Unmodeled Constraints

TL;DR

The paper introduces the CONFIG algorithm, a globally optimal constrained optimization method for closed-loop control systems with unmodeled constraints, demonstrating its effectiveness through benchmarks and real system applications.

Contribution

It presents a new provably globally optimal constrained optimization algorithm, CONFIG, for closed-loop control with unmodeled constraints, outperforming existing methods in theoretical guarantees and empirical performance.

Findings

CONFIG achieves competitive performance with CEI.

It provides a provable global optimality guarantee.

Effective in both benchmark and real control system applications.

Abstract

In this paper, the CONFIG algorithm, a simple and provably efficient constrained global optimization algorithm, is applied to optimize the closed-loop control performance of an unknown system with unmodeled constraints. Existing Gaussian process based closed-loop optimization methods, either can only guarantee local convergence (e.g., SafeOPT), or have no known optimality guarantee (e.g., constrained expected improvement) at all, whereas the recently introduced CONFIG algorithm has been proven to enjoy a theoretical global optimality guarantee. In this study, we demonstrate the effectiveness of CONFIG algorithm in the applications. The algorithm is first applied to an artificial numerical benchmark problem to corroborate its effectiveness. It is then applied to a classical constrained steady-state optimization problem of a continuous stirred-tank reactor. Simulation results show that our CONFIG algorithm can achieve performance competitive with the popular CEI (Constrained Expected Improvement) algorithm, which has no known optimality guarantee. As such, the CONFIG algorithm offers a new tool, with both a provable global optimality guarantee and competitive empirical performance, to optimize the closed-loop control performance for a system with soft unmodeled constraints. Last, but not least, the open-source code is available as a python package to facilitate future applications.