Raed I Hamed
Posters-Accepted Abstracts: Adv Robot Autom
Adaptive fuzzy Petri nets (AFPNs) model is established for velocity estimation from position values of servo motor. A graphical fuzzy model that uses rules base system to effectively process the uncertainties variables is built. Modules that represent distinct types of fuzzy rules are created and defined. An AFPN module for velocity estimation is constructed according to the structure, relations, rules, certainty factors and weights of the adaptive fuzzy model for servo motor system. The servo motor system is analysed, and definitions are prepared for the AFPN model and the input data. An AFPN model is created and trained with input data on weight w(pi). Input places are represented the membership function values the initial values of the input places are entered (position error ├?┬?├?┬Şe velocity error (├?┬Ć├?┬?e), and the output value of (p22) represented the velocity estimation . The system can perform fuzzy reasoning automatically to evaluate the degree of truth ├?┬?├?┬▒(pi)of the proposition. The presented study demonstrates that the proposed model is able to achieve the purpose of reasoning, and computing of the velocity estimation e value. An AFPN structure has been used rather than FPNs formalism to improve the efficiency of fuzzy reasoning. The effectiveness of the proposed method is verified by both model simulations and experimental results.
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