Expert Systems of Adaptive Fuzzy Reasoning-Based Implementation for Velocity Estimation of Servo Motor System

摘要

Adaptive fuzzy Petri nets (AFPNs) model isestablished for velocity estimation from position valuesof servo motor. A graphical fuzzy model that uses therule base system to effectively process the uncertaintiesvariables is built. Modules that represent distinct typesof fuzzy rules are created and defined. An AFPNmodule for velocity estimation is constructedaccording to the structure, relations, rules, certaintyfactors and weights of the adaptive fuzzy model for theservo motor system. The servo motor system isanalyzed, and definitions are prepared for the AFPNmodel and the input data. An AFPN model is createdand trained with input data on weight . Inputplaces are represented the membership function valuesthe initial values of the input places are entered(position error velocity error ), and the outputvalue of ( ) represented the velocity estimation .The system can perform fuzzy reasoning automaticallyto evaluate the degree of truth of the proposition.The presented study demonstrates that the proposedmodel is able to achieve the purpose of reasoning, andcomputing of the velocity estimation value. AnAFPN structure has been used rather than FPNsformalism to improve the efficiency of fuzzy reasoning.The effectiveness of the proposed method is verified byboth model simulations and experimental results.