Learning Model Predictive Control for Iterative Tasks. A Data-Driven Control Framework

A learning model predictive controller for iterative tasks is presented. The controller is reference-free and is able to improve its performance by learning from previous iterations. A safe set and a terminal cost function are used in order to guarantee recursive feasibility and nondecreasing perfor...

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Bibliographic Details
Published inIEEE transactions on automatic control Vol. 63; no. 7; pp. 1883 - 1896
Main Authors Rosolia, Ugo, Borrelli, Francesco
Format Journal Article
LanguageEnglish
Published IEEE 01.07.2018
Subjects
Control design
Cost function
Data driven
Iterative learning control
learning
Nonlinear dynamical systems
optimal control
Predictive control
Predictive models
safety
Trajectory
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Summary:A learning model predictive controller for iterative tasks is presented. The controller is reference-free and is able to improve its performance by learning from previous iterations. A safe set and a terminal cost function are used in order to guarantee recursive feasibility and nondecreasing performance at each iteration. This paper presents the control design approach, and shows how to recursively construct terminal set and terminal cost from state and input trajectories of previous iterations. Simulation results show the effectiveness of the proposed control logic.
ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2017.2753460