Predictive Control Barrier Functions: Enhanced Safety Mechanisms for Learning-Based Control

• 成果类型: Article
• 署名作者: Wabersich, Kim P.; Zeilinger, Melanie N.
• 署名单位: Swiss Federal Institutes of Technology Domain; ETH Zurich
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2022.3175628
• 发表日期: 2023
• 页码: 2638-2651
• 关键词: safety; Predictive control; stability analysis; Task analysis; predictive models; Prediction algorithms; Stochastic processes; Constrained control; Intelligent systems; NL predictive control; safety

摘要

While learning-based control techniques often outperform classical controller designs, safety requirements limit the acceptance of such methods in many applications. Recent developments address this issue through so-called predictive safety filters, which assess if a proposed learning-based control input can lead to constraint violations and modifies it if necessary to ensure safety for all future time steps. The theoretical guarantees of such predictive safety filters rely on the model assumptions and minor deviations can lead to failure of the filter putting the system at risk. This article introduces an auxiliary soft-constrained predictive control problem that is always feasible at each time step and asymptotically stabilizes the feasible set of the original predictive safety filter problem, thereby providing a recovery mechanism in safety-critical situations. This is achieved by a simple constraint tightening in combination with a terminal control barrier function. By extending discrete-time control barrier function theory, we establish that the proposed auxiliary problem provides a predictive control barrier function. The resulting algorithm is demonstrated using numerical examples.