Primal-Dual Contextual Bayesian Optimization for Control System Online Optimization with Time-Average Constraints

• URL: http://arxiv.org/abs/2304.06104v4
• Date: Wed, 20 Sep 2023 18:41:52 GMT
• Title: Primal-Dual Contextual Bayesian Optimization for Control System Online Optimization with Time-Average Constraints
• Authors: Wenjie Xu, Yuning Jiang, Bratislav Svetozarevic, Colin N. Jones
• Abstract summary: This paper studies the problem of online performance optimization of constrained closed-loop control systems. A primal-dual contextual Bayesian optimization algorithm is proposed that achieves sublinear cumulative regret with respect to the dynamic optimal solution.
• Score: 21.38692458445459
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper studies the problem of online performance optimization of constrained closed-loop control systems, where both the objective and the constraints are unknown black-box functions affected by exogenous time-varying contextual disturbances. A primal-dual contextual Bayesian optimization algorithm is proposed that achieves sublinear cumulative regret with respect to the dynamic optimal solution under certain regularity conditions. Furthermore, the algorithm achieves zero time-average constraint violation, ensuring that the average value of the constraint function satisfies the desired constraint. The method is applied to both sampled instances from Gaussian processes and a continuous stirred tank reactor parameter tuning problem; simulation results show that the method simultaneously provides close-to-optimal performance and maintains constraint feasibility on average. This contrasts current state-of-the-art methods, which either suffer from large cumulative regret or severe constraint violations for the case studies presented.

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