In this study, a heuristic dynamic programming controller is proposed to control a boost converter. Conventional controllers such as proportional-integral-derivative (PID) or proportional-integral (PI) are designed based on the linearized small-signal model near the operating point. Therefore, the performance of the controller during the start-up, the load change, or the input voltage variation is not optimal since the system model changes by varying the operating point. The heuristic dynamic programming controller optimally controls the boost converter by following the approximate dynamic programming. The advantage of the HDP is that the neural network-based characteristic of the proposed controller enables boost converters to easily cope with large disturbances. An HDP with a well-trained critic and action networks can perform as an optimal controller for the boost converter. To compare the effectiveness of the traditional PI-based and the HDP boost converter, the simulation results are provided.
S. Saadatmand et al., "The Heuristic Dynamic Programming Approach in Boost Converters," Proceedings of the 2020 IEEE Texas Power and Energy Conference (2020, College Station, TX), Institute of Electrical and Electronics Engineers (IEEE), Feb 2020.
The definitive version is available at https://doi.org/10.1109/TPEC48276.2020.9042517
2020 IEEE Texas Power and Energy Conference, TPEC (2020: Feb. 6-7, College Station, TX)
Electrical and Computer Engineering
Center for Research in Energy and Environment (CREE)
Keywords and Phrases
Boost; DC-DC converters; Model predictive controller; Heuristic dynamic programming; Reinforcement learning
International Standard Book Number (ISBN)
Article - Conference proceedings
© 2020 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
07 Feb 2020