Idle Speed Control of an Automotive Engine Using a Systematic Fuzzy Logic Methodology

Abstract

This paper introduces a systematic control design methodology for complex nonlinear systems hased upon the direct intelligent fuzzy logic control paradigm. A fuzzy logic control method is developed that can be used to design fuzzy logic controllers for a class of nonlinear systems. The systematic aspects of the procedure refer to an automated rule generation scheme that capitalizes upon clustering of transitional relations and their transformation from one conditional subspace to another. Elements that result from such transitions are anticipated according to the applied action of each rule. Data for this transitional set of rules can be collected via (i) a priori information such as experimental results, (ii) numerical simulations based on approximate dynamic models, and (iii) heuristics. The main advantage of the automatic rule generation scheme is that reliability of the controller performance can be potentially enhanced even in the presence of large-grained uncertainty. Such specifications of overall system performance as accuracy and precision can be arbitrarily adjusted as functions of the resolution of the design parameters. The systematic fuzzy logic control design procedure is applied to the problem of regulating the idle speed of an automotive engine. Simulation results indicate the effectiveness and efficiency of the proposed approach.