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
