文章基本信息

标题：Policy Gradient Reinforcement Learning for Uncertain Polytopic LPV Systems based on MHE-MPC
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作者：Hossein Nejatbakhsh Esfahani ; Sébastien Gros
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2022
卷号：55
期号：15
页码：1-6
DOI：10.1016/j.ifacol.2022.07.599
语种：English
出版社：Elsevier
摘要：AbstractIn this paper, we propose a learning-based Model Predictive Control (MPC) approach for the polytopic Linear Parameter-Varying (LPV) systems with inexact scheduling parameters (as exogenous signals with inexact bounds), where the Linear Time Invariant (LTI) models (vertices) captured by combinations of the scheduling parameters becomes wrong. We first propose to adopt a Moving Horizon Estimation (MHE) scheme to simultaneously estimate the convex combination vector and unmeasured states based on the observations and model matching error. To tackle the wrong LTI models used in both the MPC and MHE schemes, we then adopt a Policy Gradient (PG) Reinforcement Learning (RL) to learn both the estimator (MHE) and controller (MPC) so that the best closed-loop performance is achieved. The effectiveness of the proposed RL-based MHE/MPC design is demonstrated using an illustrative example.
关键词：KeywordsModel Predictive ControlMoving Horizon EstimationReinforcement LearningPolytopic LPVMulti-Model Linear System