Abstract
“SIRMs Dynamically Connected Fuzzy Inference Model” is proposed for plural input fuzzy control. For each input item, a SIRM (Single Input Rule Module) is constructed, and a dynamic importance degree is defined by a sum of a base value and a dynamic value. The base value is used to insure the role of the input item at steady state, while the dynamic value is designed to change with control situation so that the importance degree is tuned in real time. At each sampling time, the model output is obtained by summarizing the products of the dynamic importance degree and the fuzzy inference result of each module. The concrete design method of the proposed model is also given when constant value control plants are taken into consideration, and it is made clear that each dynamic value can be determined just based on the current local information of the corresponding input item. Furthermore, the proposed model is proved to be nonlinear PI control when it adopts the output error and the first-order change in the error as its input items to control first-order plants, and nonlinear PID control when it adopts the output error, the first-order change in the error, the second change in the error as its input items to control second-order plants. The proposed model is applied to typical first-order lag plants and second-order lag plants. The simulation results show that the reaching time can be reduced without any steady-state error, overshoot, or vibration compared with the conventional linear PI or PID controller.
