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Neural Computing and Applications

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04.02.2021 | Original Article

Optimal tracking control of switched systems applied in grid-connected hybrid generation using reinforcement learning

verfasst von: Jiayue Sun, Huaguang Zhang, Yingchun Wang, Mingrui Fu

Erschienen in: Neural Computing and Applications | Ausgabe 15/2021

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Abstract

The paper presents a reinforcement learning approach for optimal tracking control of switched systems with application to a grid-tied hybrid generation system. To enhance interaction with the irregular environment, reference trajectory is learned via controller from states to optimal control. The main issue is to solve the optimal tracking control problem for a hybrid generation system consisting of multiple switched subsystems, and reinforcement learning can seek the globally optimal solution well without knowing accurate system dynamics. The investigated learning algorithm is used to generate an optimum map based on the learned ultimate value without knowledge of system parameters and obtains the optimal control law via deriving of algebraic Riccati equation (ARE) with unnecessary knowing of command generator dynamics. The optimal control solution can converge the online learning algorithm well based on policy iteration as verification in the simulation.

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Titel: Optimal tracking control of switched systems applied in grid-connected hybrid generation using reinforcement learning
verfasst von: Jiayue Sun
Huaguang Zhang
Yingchun Wang
Mingrui Fu
Publikationsdatum: 04.02.2021
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 15/2021
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-021-05696-2

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