Adaptive Critic Learning Techniques for Engine Torque and Air-Fuel Ratio Control

A new approach for engine calibration and control is proposed. In this paper, we present our research results on the implementation of adaptive critic designs for self-learning control of automotive engines. A class of adaptive critic designs that can be classified as (model-free) action-dependent h...

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Bibliographic Details
Published in:IEEE transactions on cybernetics 2008-08, Vol.38 (4), p.988-993
Main Authors: Derong Liu, Javaherian, H., Kovalenko, O., Huang, T.
Format: Article
Language:English
Subjects:
Adaptive control
Adaptive critic designs (ACDs)
adaptive dynamic programming
Air
air-fuel ratio (AFR) control
Algorithms
approximate dynamic programming
Artificial Intelligence
automotive engine control
Automotive engineering
Calibration
Computer Simulation
Control design
Dynamic programming
Electric Power Supplies
Engines
Feedback
Fuel Oils
Models, Theoretical
Neural networks
Optimal control
Programmable control
Programming, Linear
Studies
Torque
Torque control
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Summary:A new approach for engine calibration and control is proposed. In this paper, we present our research results on the implementation of adaptive critic designs for self-learning control of automotive engines. A class of adaptive critic designs that can be classified as (model-free) action-dependent heuristic dynamic programming is used in this research project. The goals of the present learning control design for automotive engines include improved performance, reduced emissions, and maintained optimum performance under various operating conditions. Using the data from a test vehicle with a V8 engine, we developed a neural network model of the engine and neural network controllers based on the idea of approximate dynamic programming to achieve optimal control. We have developed and simulated self-learning neural network controllers for both engine torque (TRQ) and exhaust air-fuel ratio (AFR) control. The goal of TRQ control and AFR control is to track the commanded values. For both control problems, excellent neural network controller transient performance has been achieved.
ISSN:1083-4419
2168-2267
1941-0492
2168-2275
DOI:10.1109/TSMCB.2008.922019