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A Rapid Method for Modeling a Variable Cycle Engine
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Published on: August 13, 2019

Adaptive critic learning techniques for engine torque and air-fuel ratio control.

Derong Liu1, Hossein Javaherian, Olesia Kovalenko

  • 1Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA. dliu@ece.uic.edu

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|July 18, 2008
PubMed
Summary

This study introduces adaptive critic designs for self-learning automotive engine control, achieving optimal performance and reduced emissions. The developed neural network controllers demonstrated excellent transient performance for engine torque and air-fuel ratio regulation.

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Area of Science:

  • Automotive Engineering
  • Control Systems
  • Artificial Intelligence

Background:

  • Traditional engine calibration faces challenges in optimizing performance and reducing emissions across diverse operating conditions.
  • Self-learning control systems offer a promising avenue for adaptive engine management.
  • Adaptive Critic Designs (ACDs) provide a framework for intelligent control based on dynamic programming principles.

Purpose of the Study:

  • To implement and evaluate adaptive critic designs for self-learning control of automotive engines.
  • To improve engine performance, reduce emissions, and maintain optimal operation under various conditions.
  • To develop and simulate neural network controllers for precise engine torque (TRQ) and exhaust air-fuel ratio (AFR) control.

Main Methods:

  • Utilized a class of model-free action-dependent heuristic dynamic programming (ADHDP) within the adaptive critic designs framework.
  • Developed a neural network model of a V8 engine using test vehicle data.
  • Designed and simulated self-learning neural network controllers based on approximate dynamic programming (ADP).

Main Results:

  • Successfully developed and simulated self-learning neural network controllers for both engine torque and exhaust air-fuel ratio control.
  • Achieved excellent transient performance in tracking commanded values for both TRQ and AFR control.
  • Demonstrated the potential for improved engine performance and reduced emissions through adaptive control.

Conclusions:

  • Adaptive critic designs, specifically ADHDP, are effective for self-learning control of automotive engines.
  • Neural network controllers based on approximate dynamic programming can achieve precise and responsive engine control.
  • The proposed approach holds significant promise for enhancing automotive engine efficiency and environmental impact.