Safe Trajectory Tracking in Uncertain Environments

• 成果类型: Article
• 署名作者: Batkovic, Ivo; Ali, Mohammad; Falcone, Paolo; Zanon, Mario
• 署名单位: Chalmers University of Technology; Chalmers University of Technology; Universita di Modena e Reggio Emilia; IMT School for Advanced Studies Lucca
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2022.3207875
• 发表日期: 2023
• 页码: 4204-4217
• 关键词: Flexible trajectory tracking; nonlinear model predictive control (MPC); recursive feasibility; safety; STABILITY; uncertain constraints

摘要

In the model predictive control formulations of trajectory tracking problems, infeasible reference trajectories and a priori unknown constraints can lead to cumbersome designs, aggressive tracking, and loss of recursive feasibility. This is the case, for example, in trajectory tracking applications for mobile systems in the presence of constraints that are not fully known a priori. In this article, we propose a new framework called model predictive flexible trajectory tracking control, which relaxes the trajectory tracking requirement. In addition, we accommodate recursive feasibility in the presence of a priori unknown constraints, which might render the reference trajectory infeasible. In the proposed framework, constraint satisfaction is guaranteed at all times while the reference trajectory is tracked as good as constraint satisfaction allows, thus simplifying the controller design and reducing possibly aggressive tracking behavior. The proposed framework is illustrated with three numerical examples.