文章基本信息

标题：Practical method to complete Learning Model Predictive Control with generalization capability
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作者：Ferenc Török ; Tamás Péni
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：14381-14386
DOI：10.1016/j.ifacol.2020.12.1395
语种：English
出版社：Elsevier
摘要：AbstractThe paper presents a practical method to complete Learning Model Predictive Control (LMPC) with generalization capability. LMPC has been developed by F. Borrelli and his co-authors for systems performing iterative tasks. The method is based on saving the state trajectories of successful runs and using this database to improve the control performance in the future iterations. When the controller faces a new task, the database is cleared and the learning phase starts over. This paper addresses the question of how a general knowledge base can be built to warm start the learning process. As a potential solution, a practical method is proposed. The algorithm is tailored specifically to the autonomous racing application, but the concept can be extended to a wider class of control problems. The procedure includes the construction of special teaching tracks, on which the trajectory database is generated and a multi-step migration procedure for transferring the learned trajectories onto any new track. The efficiency of the method is demonstrated by numerical simulations.
关键词：Keywordsmodel predictive controlnonlinear controliterativerepetitive learning controlautonomous racing