A proposal of SIRMs dynamically connected fuzzy inference model for plural input fuzzy control
Single input rule modules (SIRMs) dynamically connected fuzzy inference model is proposed for plural input fuzzy control. For each input item, a SIRM is constructed and a dynamic importance degree is defined. The dynamic importance degree consists of a base value insuring the role of the input item through a control process, and a dynamic value changing with control situations to adjust the dynamic importance degree. Each dynamic value can be easily tuned based on the local information of current state. The model output is obtained by summarizing the products of the dynamic importance degree and the fuzzy inference result of each SIRM. The controller constructing method for constant value control systems is given, and constant value controls of typical first- and second-order lag plants are tested. The simulation results show that by using the proposed mode, the reaching time can be reduced by more than 15% without any steady-state error, overshoot, or vibration compared with the SIRMs fixed importance degree connected fuzzy inference model. The proposed model is further successfully applied to stabilization control of an inverted pendulum system including the position control of the cart. (C) 2002 Elsevier Science B.V. All rights reserved.
sirm fuzzy inference model fuzzy control dynamic importance degree pendulum process control