文章基本信息

标题：Task Decomposition for MPC: A Computationally Efficient Approach for Linear Time-Varying Systems ⁎
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作者：Charlott Vallon ; Francesco Borrelli
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：4240-4245
DOI：10.1016/j.ifacol.2020.12.2574
语种：English
出版社：Elsevier
摘要：AbstractA Task Decomposition method for iterative learning Model Predictive Control (TDMPC) for linear time-varying systems is presented. We consider the availability of state-input trajectories which solve an original taskτ1, and design a feasible MPC policy for a new task,τ2, using stored data fromτ1. Our approach applies to tasksτ2which are composed of subtasks contained inτ1. In this paper we formally define the task decomposition problem, and provide a feasibility proof for the resulting policy. The proposed algorithm reduces the computational burden for linear time-varying systems with piecewise convex constraints. Simulation results demonstrate the improved efficiency of the proposed method on a robotic path-planning task.
关键词：KeywordsData-based controlIterativeRepetitive Learning controlLinear Model Predictive ControlConvex Optimization