Liu Derong, Javaherian Hossein, Kovalenko Olesia, Huang Ting
Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA.
IEEE Trans Syst Man Cybern B Cybern. 2008 Aug;38(4):988-93. doi: 10.1109/TSMCB.2008.922019.
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.
提出了一种发动机校准与控制的新方法。在本文中，我们展示了关于将自适应评判设计应用于汽车发动机自学习控制的研究成果。本研究项目采用了一类可归类为（无模型）基于动作的启发式动态规划的自适应评判设计。当前汽车发动机学习控制设计的目标包括提高性能、减少排放以及在各种工况下保持最佳性能。利用一辆配备V8发动机的测试车辆的数据，我们基于近似动态规划的理念开发了发动机神经网络模型和神经网络控制器，以实现最优控制。我们已经开发并模拟了用于发动机扭矩（TRQ）和排气空燃比（AFR）控制的自学习神经网络控制器。TRQ控制和AFR控制的目标是跟踪指令值。对于这两个控制问题，均已实现了出色的神经网络控制器瞬态性能。
