MINIMIZING PEAK OVERSHOOT WITH REDUCED RULE BASE ON PID CONTROLLER OF SWITCHED RELUCTANCE MOTOR DRIVE
electrical machines and drives
Switched Reluctance Motor (SRM) is a machine where electromagnetic torque is designed and developed with variable changes in the reluctance process. Both stator and rotor has salient poles but only stator carries windings, hence a steady state error occurs in higher ratio on the Proportional-Integral-Derivative (PID) Controller. PID controller with variable gains on SRM consumes more peak overshoot time on the limited DC voltage supply saturation on specific devices. PID Fuzzy Controller for SRM shows the response time with very little bit variation over different load disturbances. But, the rise time on high speed electronics SRM drive fails to respond the circuit to fast input signals. All these limitations, planned to set up a SRM system design with PID Neuro Fuzzy Controller. To overcome high peak overshoot time on varying load disturbances, a framework called Neural Learning of PID Neuro Fuzzy based onCondensed Multi layer Perceptrons (NLPID Controller-NFCMP) is proposed in this paper. Initially, PID Neuro Fuzzy Controller uses the neural network learning techniques to tune the fuzzy membership function. The integration of neural network and fuzzy logic with the PID controller helps in handling multiple speed SRM drive machines. Here, multiple input points are taken and dynamic output system is generated with if-then rule fuzzification. Second, the Neuro fuzzy membership function with 49 rules in NLPID Controller-NFCMP framework is tuned to 12 rules using the Condensed M
This article is written in Adobe PDF format ( .pdf file ).To view this article you need to download the file. Please rightclick on the link below and then select "Save
target as" to download the file to your harddrive.