论文标题
MPC的任务分解：线性时变系统的计算高效方法
Task Decomposition for MPC: A Computationally Efficient Approach for Linear Time-Varying Systems
论文作者
论文摘要
提出了用于迭代学习模型预测控制（TDMPC）的任务分解方法。我们考虑使用来自T1的存储数据的新任务T2的可行MPC策略，考虑解决原始任务T1的状态输入轨迹的可用性。我们的方法适用于由T1中包含的子任务组成的任务T2。在本文中，我们正式定义了任务分解问题，并为结果策略提供了可行性证明。提出的算法减少了具有分段凸约限制的线性时变系统的计算负担。仿真结果证明了在机器人路径规划任务上提出的方法的提高效率。
A 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 task T1, and design a feasible MPC policy for a new task, T2, using stored data from T1. Our approach applies to tasks T2 which are composed of subtasks contained in T1. 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.
