title: Adaptive critic learning techniques for engine torque and air-fuel ratio control.
abstract: 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.
authors.affiliation: Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA. dliu@ece.uic.edu
authors.forename: Derong
authors.initials: D
authors.lastname: Liu
authors.forename: Hossein
authors.initials: H
authors.lastname: Javaherian
authors.forename: Olesia
authors.initials: O
authors.lastname: Kovalenko
authors.forename: Ting
authors.initials: T
authors.lastname: Huang
mesh_terms: D000388:Air; D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D011211:Electric Power Supplies; D005246:Feedback; D005648:Fuel Oils; D008962:Models, Theoretical; D011382:Programming, Linear; D019415:Torque
chemical_list: D005648:Fuel Oils