Switched Reluctance Motor Torque Ripples Reduction by the Aid Of Adaptive Reference Model
- Resource Type
- Conference
- Authors
- Pavlitov, C.; Chen, H.; Gorbounov, Y.; Tashev, T.; Georgiev, T.; Xing, W.
- Source
- SPEEDAM 2010 Power Electronics Electrical Drives Automation and Motion (SPEEDAM), 2010 International Symposium on. :1276-1279 Jun, 2010
- Subject
- Power, Energy and Industry Applications
Robotics and Control Systems
Reluctance motors
Torque
Reluctance machines
Neural networks
Mathematical model
Artificial neural networks
Nonlinear equations
Rotors
Neurofeedback
Voltage
ART neural networks
Rotating machine nonlinear analysis
Reluctance motor drives
Model reference adaptive control
- Language
This paper deals with SRM torque ripples reduction. They are one of the major obstacles for SRM wide spread application. Direct torque feedback is the best solution for the case but due to the price it is quite unacceptable. Feedback with neural network torque estimator reduces torque ripples up to 2.5 times comparing them to the opened system but in many cases this reduction is insufficient. That is why very precise parallel running neural network motor model has been suggested. This model copies the behavior of the SRM and can be exploited as an observer of different state parameters including the dynamic torque. Applying this model, it has been discovered that certain overlapping of the phase voltages can reduce 10 times torque ripples comparing them to the regular opened system. In fact, this adaptive reference model is fully implementable by means of parallel running algorithms embedded in middle sized FPGA.