Adaptive Stochastic MPC Under Time-Varying Uncertainty

• 成果类型: Article
• 署名作者: Bujarbaruah, Monimoy; Zhang, Xiaojing; Tanaskovic, Marko; Borrelli, Francesco
• 署名单位: University of California System; University of California Berkeley
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2020.3009362
• 发表日期: 2021
• 页码: 2840-2845
• 关键词: uncertainty; Adaptation models; Adaptive systems; stability analysis; Time-varying systems; Robustness; Stochastic processes; Adaptive predictive control; disturbance feedback; online learning; stochastic model-predictive control (MPC)

摘要

This article deals with the problem of formulating an adaptive model-predictive control strategy for constrained uncertain systems. We consider a linear system in the presence of bounded time-varying additive uncertainty. The uncertainty is decoupled as the sum of a process noise with known bounds and a time-varying offset that we wish to identify. The time-varying offset uncertainty is assumed unknown pointwise in time. Its domain, called the feasible parameter set, and its maximum rate of change are known to the control designer. As new data become available, we refine the feasible parameter set with a set-membership-method-based approach, using the known bounds on process noise. We consider the case of probabilistic constraints on system states, with hard constraints on actuator inputs. We robustly satisfy the imposed constraints for all possible values of the offset uncertainty in the feasible parameter set. By imposing adequate terminal conditions, we prove recursive feasibility and stability of the proposed algorithm. The efficacy of the proposed approach is illustrated with a detailed numerical example.