Search Results

Fuzzy control is based on either expert knowledge or experimental data and, therefore, it possesses intrinsic qualities like robustness and ease of implementation. The mathematical modeling for fuzzy control systems has been attempted, but until now many models that have been developed do not extend beyond the application for which they were developed. A general class of fuzzy linguistic control algorithms that can be formulated analytically and can capture the nonlinear aspect of a given fuzzy control scheme has been formulated using interpolating functions. The interpolating functions map the process error and its rate or it's a cumulative sum into control action. The systematic approach makes it more desirable to be used in the control of nonlinear dynamics systems. The analytical method developed in previous wok conducted by Langari (1992) and modified by the authors is employed in this practical example to design a controller for an idle speed control system.

In this study, an adaptive, supervisory, and hybrid fuzzy controllers as well as a fuzzy estimator are developed based on experimental and simulation data and expert's knowledge of the normal working of diagnostic systems. The advantage of the developed control system is that it has fewer parameters to tune; it also makes the system adaptable for changing environments or malfunctions; and provides the supervision of the overall performance of the engine management system. For the fuzzy estimator part of the system we employ the least square and gradient methods for the premise and consequent of the rule. This study introduces a new design method for FDI module. It also provides an efficient and easy to implement control system that can be incorporated into existing module with minimum changes, adjustment, and cost.