• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.13 no.4
• /
• pp.109-119
• /
• 1999

Switched Reluctance Motor(SRM) have been expanding gradually their awlications in the variable speed drives due to their relatively low cost, simple and robust structure, controllability and high efficiency. In this paper neural network theory is used to detemrine fuzzy-neural network controller's membership ftmctions and fuzzy rules. In addition neural network emulator is used to emulate forward dynamics of SRM and to get error signal at fuzzy-neural controller output layer. Error signal is backpropagated through neural network emulator. The backpropagated error of emulator offers the path which reforms the fuzzy-neural network controller's mmbership ftmctions and fuzzy rules. 32bit Digital Signal Processor(TMS320C31) was used to achieve the high speed control and to realize the fuzzy-neural control algorithm. Simulation and experimental results show that in the case of load variation the proposed control rrethcd was superior to a conventional rrethod in the respect of speed response.sponse.

• The Transactions of the Korean Institute of Power Electronics
• /
• v.6 no.1
• /
• pp.98-106
• /
• 2001

This paper presents external load disturbance compensation that used to deadbeat load torque observer and regulation of the compensation gain by parameter estimator. As a result, the response of PMSM follows that of the nominal plant. The load torque compensation method is compose of a dead beat observer that is well-known method. However it has disadvantage such as a noise amplification effect. To reduce of the effect, the post-filter, which is implemented by MA process, is proposed. The parameter compensator with RLSM(recursive least square method) parameter estimator is suggested to increase the performance of the load torque observer and main controller. Although RLSM estimator is one of the most effective methods for online parameter identification, it is difficult to obtain unbiased result in this application. It is caused by disturbed dynamic model with external torque. The proposed RLSM estimator is combined with a high performance torque observer to resolve the problems. As a result, the proposed control system becomes a robust and precise system against the load torque and the parameter variation. A stability and usefulness, through the verified computer simulation and experiment, are shown in this paper.