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Minimum time learning model predictive control
Author(s) -
Rosolia Ugo,
Borrelli Francesco
Publication year - 2021
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.5284
Subject(s) - mathematical optimization , model predictive control , computer science , constraint (computer aided design) , nonlinear system , constraint satisfaction , function (biology) , terminal (telecommunication) , convergence (economics) , class (philosophy) , set (abstract data type) , obstacle , construct (python library) , regular polygon , control (management) , mathematics , artificial intelligence , telecommunications , physics , geometry , quantum mechanics , evolutionary biology , probabilistic logic , political science , law , economics , biology , programming language , economic growth
Summary In this paper we present a Learning Model Predictive Control (LMPC) strategy for linear and nonlinear time optimal control problems. Our work builds on existing LMPC methodologies and it guarantees finite time convergence properties for the closed‐loop system. We show how to construct a time varying safe set and terminal cost function using closed‐loop data. The resulting LMPC policy is time varying and it guarantees recursive constraint satisfaction and non‐decreasing performance. Computational efficiency is obtained by convexifing the time‐varying safe set and time‐varying terminal cost function. We demonstrate that, for a class of nonlinear system and convex constraints, the convex LMPC formulation guarantees recursive constraint satisfaction and nondecreasing performance. Finally, we illustrate the effectiveness of the proposed strategies on minimum time obstacle avoidance and racing examples.