Automotive Engine Idle Speed Control with Recurrent Neural Networks

Abstract

This paper describes the development of recurrent neural network controllers for an automotive engine idle speed control (ISC) problem. Engine ISC is a difficult problem because of troublesome characteristics such as severe process nonlinearities, variable time delays, time-varying process dynamics and unobservable system states and disturbances. We demonstrate that recurrent neural network controllers can be trained to handle these difficulties gracefully while achieving good regulator performance for a representative model of 4-cylinder, 1.6 liter engine. Empirical results clearly illustrate that neural network controllers with relatively large amounts of internal feedback provide more robust performance for the ISC problem than do neural network controllers that are static or contain limited internal recurrent connections.