Computing Optimal Joint Chance Constrained Control Policies

• 成果类型: Article
• 署名作者: Schmid, Niklas; Fochesato, Marta; Li, Sarah H. Q.; Sutter, Tobias; Lygeros, John
• 署名单位: Swiss Federal Institutes of Technology Domain; ETH Zurich; University of Konstanz
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2025.3546078
• 发表日期: 2025
• 页码: 4904-4911
• 关键词: safety; COSTS; trajectory; Aerospace electronics; Stochastic processes; programming; optimal control; dynamic programming; kernel; Heuristic algorithms; Dynamic programming (DP); joint chance constrained programming; stochastic optimal control

摘要

We consider the problem of optimally controlling stochastic, Markovian systems subject to joint chance constraints over a finite-time horizon. For such problems, standard dynamic programming is inapplicable due to the time correlation of the joint chance constraints, which calls for non-Markovian, and possibly stochastic, policies. Hence, despite the popularity of this problem, solution approaches capable of providing provably optimal and easy-to-compute policies are still missing. We fill this gap by augmenting the dynamics via a binary state, allowing us to characterize the optimal policies and develop a dynamic programming-based solution method.