Learning Model Predictive Control for iterative tasks. A Data-Driven Control Framework

摘要

A Learning Model Predictive Controller (LMPC) for iterative tasks ispresented. The controller is reference-free and is able to improve itsperformance by learning from previous iterations. A safe set and a terminalcost function are used in order to guarantee recursive feasibility andnon-increasing performance at each iteration. The paper presents the controldesign approach, and shows how to recursively construct terminal set andterminal cost from state and input trajectories of previous iterations.Simulation results show the effectiveness of the proposed control logic.