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

2016/09/06 by Ugo Rosolia, Rosolia, Ugo, Francesco Borrelli +1 · 6 citations
Engineering · #Advanced Control Systems Optimization #Control Systems and Identification #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Mathematics #Fault Detection and Control Systems #Machine Learning (cs.LG) #Optimization and Control (math.OC) #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.1609.01387

openalex publication_date 2016/09/06 · openalex created_date 2022/10/02 · openalex updated_date 2026/07/28

Abstract

A Learning Model Predictive Controller (LMPC) for iterative tasks is\npresented. The controller is reference-free and is able to improve its\nperformance by learning from previous iterations. A safe set and a terminal\ncost function are used in order to guarantee recursive feasibility and\nnon-increasing performance at each iteration. The paper presents the control\ndesign approach, and shows how to recursively construct terminal set and\nterminal cost from state and input trajectories of previous iterations.\nSimulation results show the effectiveness of the proposed control logic.\n

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