A Hybrid End-to-End Control Strategy Combining Dueling Deep Q-network and PID for Transient Boost Control of a Diesel Engine with Variable Geometry Turbocharger and Cooled EGR

Bo Hu, Jiaxi Li, Shuang Li and Jie Yang
Additional contact information
Bo Hu: Key Laboratory of Advanced Manufacturing Technology for Automobile Parts, Ministry of Education, Chongqing University of Technology, Chongqing 400054, China
Jiaxi Li: Key Laboratory of Advanced Manufacturing Technology for Automobile Parts, Ministry of Education, Chongqing University of Technology, Chongqing 400054, China
Shuang Li: Key Laboratory of Advanced Manufacturing Technology for Automobile Parts, Ministry of Education, Chongqing University of Technology, Chongqing 400054, China
Jie Yang: Key Laboratory of Advanced Manufacturing Technology for Automobile Parts, Ministry of Education, Chongqing University of Technology, Chongqing 400054, China

Energies, 2019, vol. 12, issue 19, 1-15

Abstract: Deep reinforcement learning (DRL), which excels at solving a wide variety of Atari and board games, is an area of machine learning that combines the deep learning approach and reinforcement learning (RL). However, to the authors’ best knowledge, there seem to be few studies that apply the latest DRL algorithms on real-world powertrain control problems. If there are any, the requirement of classical model-free DRL algorithms typically for a large number of random exploration in order to realize good control performance makes it almost impossible to implement directly on a real plant. Unlike most of the other DRL studies, whose control strategies can only be trained in a simulation environment—especially when a control strategy has to be learned from scratch—in this study, a hybrid end-to-end control strategy combining one of the latest DRL approaches—i.e., a dueling deep Q-network and traditional Proportion Integration Differentiation (PID) controller—is built, assuming no fidelity simulation model exists. Taking the boost control of a diesel engine with a variable geometry turbocharger (VGT) and cooled (exhaust gas recirculation) EGR as an example, under the common driving cycle, the integral absolute error (IAE) values with the proposed algorithm are improved by 20.66% and 9.7% respectively for the control performance and generality index, compared with a fine-tuned PID benchmark. In addition, the proposed method can also improve system adaptiveness by adding another redundant control module. This makes it attractive to real plant control problems whose simulation models do not exist, and whose environment may change over time.

Keywords: hybrid control strategy; dueling deep Q-network; PID; transient boost control; VGT (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/12/19/3739/pdf (application/pdf)
https://www.mdpi.com/1996-1073/12/19/3739/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:12:y:2019:i:19:p:3739-:d:272288

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().