In this paper, a dual heuristic 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 start-up, load change, or input voltage variation is not optimal since the system model changes by varying the operating point. The dual heuristic programming controller optimally controls the boost converter by following the approximate dynamic programming. The advantage of the DHP is that the neural network–based characteristic of the proposed controller enables boost converters to easily cope with large disturbances. A DHP 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 DHP boost converter, the simulation results are provided.

Meeting Name

10th Annual Computing and Communication Workshop and Conference, CCWC (2020: Jan. 6-8, Las Vegas, NV)


Electrical and Computer Engineering

Keywords and Phrases

Adaptive critic design; Boost converter; DC–DC converters; Model predictive controller; Dual heuristic 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

08 Jan 2020