An automated rule design of fuzzy logic controllers for uncertain dynamic systems

The author investigates a systematic design procedure for automated rule generation for fuzzy-logic-based controllers of uncertain dynamic systems such as an engine dynamic model. Automated rule generation means autonomous clustering or collection of meaningful transitional relations from one conditional subspace to another. During the design procedure, optimal control strategies such as minimum squared error, near minimum time, minimum energy, or combined performed criteria are considered. Fuzzy feedback control systems designed by the cell-state transition method have the properties of closed-loop stability, robustness under parameter variations, and a certain degree of optimality. The main advantage of the approach is that reliability can potentially be increased even if a large grain of uncertainty is involved within the control system under consideration. A numerical example is shown in which the strategic fuzzy controller design is applied to a highly nonlinear model of engine idle-speed control