| Summary: | 碩士 === 國立高雄應用科技大學 === 電機工程系 === 98 === This paper presents a dynamic modeling and intelligent control method for two different type actuators, the permanent magnet synchronous motor (PMSM) and the traveling wave ultrasonic motor (TWUSM). In the dynamic modeling and drive system of permanent magnet synchronous motor, the precise mathematical model has been derived by using a field-oriented control method and combination of sinusoidal pulse width modulation (SPWM) strategies, which are easily to construct the whole simulation system in the Matlab Simulink. For the dynamic modeling and drive system of traveling wave ultrasonic motor two-dimensional analytical method with LLCC resonant driving circuit are adopted.
In control aspect, this paper proposed a self-constructing and simplification recurrent fuzzy neural network for the speed control of two different motors to trace periodic reference trajectories. The proposed learning algorithm consists of structure learning and parameter learning. The structure learning determines neurons (fuzzy rules) generation, while the parameter learning algorithm used the supervised gradient decent method to adjust the connected weights in the consequent part. When the system is stable, fuzzy decision-making method is used to delete unimportant fuzzy rules automatically, so as to achieve a simplest structure of SCRFNN. Even confront with different system, the proposed method may create a suitable fuzzy rule base according to the characteristics of each system, and show a good control performance.
Finally, this article use Simulink to simulation the part of dynamic modeling, drive system and control of two kinds motors. After comparing simulation results with the drive system base on GA-Fuzzy control system, it can be verified the proposed control method in face of two completely different drives systems, still have good speed and high precision tracking response. When the system is subjected to load and parameter changes, it also exists good robustness.
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