1998 Volume 10 Issue 6 Pages 1175-1181
Although the SIRMs dynamically connected fuzzy inference model can improve drastically the control performance of first-order delay plants and second-order delay plants compared with the SIRMs connected fuzzy inference model, no systematic method of determining the parameters of its dynamic importance degrees is established. In this paper the model is applied to first-order delay plants, and a fuzzy controller is constructed taking the output error and the change in the output error as the input items and the change in the manipulated variable as the output item. It is proved that the fuzzy controller completely corresponds to the conventional PI controller in each control action, and is essentially a nonlinear PI controller. For first-order delay plants with a time constant from 0.20 to 30.00, the setting method of the parameters, i.e., the base values and the changing breadths, of the dynamic importance degrees are given based on the data collected by the random optimization search method. Simulation results show that by using the proposed method, the plant output can rise to a desired value quickly, and the overshoot or undershoot amount is suppressed to about 1.0% without vibration or steady-state error.
