Browsing by Author "Wen, Jung-Sheng"

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Intelligent Control of High-Speed Sensorless Brushless DC Motor for Intelligent Automobiles
(2008)
Wen, Jung-Sheng
;
Wang, Chi-Hsu
;
Chang, Ying-De
;
Teng, Ching-Cheng
;
電控工程研究所
For the last decade, the need for electric driven vehicle has risen rapidly due to the global warming problem. A high efficient motor need to be developed to replace traditional engine. In this paper, the authors present a fuzzy logic controller (FLC) for sensorless brushless dc (BLDC) motor. The proposed FLC is based on the terminal voltage measurement as the sensorless method for BLDC motor control. There are two modules designed in the speed control of BLDC motor, i.e. command and regulating modules. Command module is to find and issue commutation period and PWM duty cycle to the BLDC motor for desired speed. The regulating module is designed by applying a FLC with sensorless tenchnology and is used to regulate the speed of BLDC motor under various disturbances, such as loading effect. Two resistances are adopted to detect the zero-crossing point (ZCP) of back-EMF signal on the unexcited phase instead of using the expensive Hall sensors. The regulating module can regulate accurately and quickly the PWM duty cycle by fuzzy reasoning which makes the BLDC motor rotate smoothly at desired rotation speed even there exists an external disturbance. In order to reduce the computational load of realtime FLC for the microcontroller C8051F330, we can perform the FLC computation off-line to cover nearly all cases. These data will be recorded and organized as a Look-Up Table (LUT). This paper shows that a regulating module designed by FLC is better than a none regulation module or a regulating module with a P controller.
On the equivalence of a table lookup (TL) technique and fuzzy neural network (FNN) with block pulse membership functions (BPMFs) and its application to water injection control of an automobile
(2008-07-01)
Wang, Chi-Hsu
;
Wen, Jung-Sheng
;
電控工程研究所
;
Institute of Electrical and Control Engineering
This paper presents an alternative method to design a fuzzy neural network (FNN) using a set of nonoverlapped block pulse membership functions (BMPFs), and this FNN with nonoverlapped BPMFs will be shown to be equivalent to the conventional table lookup (TL) technique. Therefore, the hidden links between TL and FNN techniques are revealed in this paper that provides a methodology to design a TL controller based on the FNN design concept. In order to do so, a new direct formula is first developed to generate the fuzzy rules from the premise part in FNN. This direct formula not only guarantees a one-to-one mapping that maps the fuzzy membership functions onto the fuzzy rules, but also alleviates the coding effort during hardware implementation. It is further elaborated that the FNN with nonoverlapped BPMFs has the advantage of faster online training that requires less computation time, but at the cost of more memory requirement to store the fuzzy rules. The application of this new approach has been applied successfully in the water injection control of a turbo-charged automobile with excellent results.