An Explicit Dual Control Approach for Constrained Reference Tracking of Uncertain Linear Systems

• 成果类型: Article
• 署名作者: Parsi, Anilkumar; Iannelli, Andrea; Smith, Roy S.
• 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.2022.3176800
• 发表日期: 2023
• 页码: 2652-2666
• 关键词: uncertainty; Measurement uncertainty; Linear systems; Electron tubes; Robustness; Adaptive control; uncertain systems; Active learning; Dual control; model predictive control; reference tracking; safe adaptive control

摘要

A finite horizon optimal tracking problem is considered for linear dynamical systems subject to parametric uncertainties in the state-space matrices and exogenous disturbances. A suboptimal solution is proposed using a model predictive control (MPC) based explicit dual control approach, which enables active uncertainty learning. A novel algorithm for the design of robustly invariant online terminal sets and terminal controllers is presented. Set membership identification is used to update the parameter uncertainty online. A predicted worst-case cost is used in the MPC optimization problem to model the dual effect of the control input. The cost-to-go is estimated using contractivity of the proposed terminal set and the remaining time horizon, so that the optimizer can estimate future benefits of exploration. The proposed dual control algorithm ensures robust constraint satisfaction and recursive feasibility, and navigates the exploration-exploitation tradeoff using a robust performance metric.