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.

Meeting Name

2020 IEEE Texas Power and Energy Conference, TPEC (2020: Feb. 6-7, College Station, TX)


Electrical and Computer Engineering

Research Center/Lab(s)

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)


Document Type

Article - Conference proceedings

Document Version

Accepted Manuscript

File Type





© 2020 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

07 Feb 2020