Design of Adaptive Intelligent Controller for Vector Controlled Induction Motor Drives

• Main Authors: Chun-Jung Chiu, 邱俊榮
• Other Authors: Chwan-Lu Tseng
• Format: Others
• Language: zh-TW
• Online Access: http://ndltd.ncl.edu.tw/handle/h5ps8d

博士 === 國立臺北科技大學 === 電機工程研究所 === 103 === The induction motors (IMs) have numerous advantages, such as rugged structure, low cost, no mechanical commutators and brush assemblies. However, the mathematical model of the IM is nonlinear, and states are coupling, which make controlling of IM difficult. To solve this problem, this study adopted a vector-control IM drive technique. However, the nonlinear characteristics and its inherent uncertainties of the IM, continue to make controlling a vector control-based IM drive a challenge. Therefore, an intelligent control method must be developed for designing speed controllers and observers for vector controlled IM drives. In this study, the adaptive Takagi-Sugeno-Kang fuzzy cerebellar model articulation controller (adaptive TSK-FCMAC) is proposed, which is designed using TSK fuzzy theory and adaptive cerebellar model articulation controller (adaptive CMAC) in a sensorless vector control for IM drives. The proposed controller comprises a compensating controller and the TSK-FCMAC. With an auxiliary compensator, the TSK-FCMAC is capable of offering compensation efforts that are more flexible for improving the transient responses of the IM. The adaptive TSK-FCMAC was derived using the Lyapunov approach and guaranteed learning-error convergence. According to model reference adaptive system theory, TSK-fuzzy control was used to design the adaptive TSK-fuzzy speed and rotor resistance observers (ATSKFO) for establishing a speed sensorless control. In addition, the projection algorithm of the adaptive theory was adopted for modifying the consequence part of the TSK fuzzy rule parameters. The speed and rotor resistance estimated using the proposed observers were fed back to the adaptive TSK-FCMAC and adaptive pseudo-reduced-order flux observer (APRO) to achieve the adaptive vector control. Finally, to apply the proposed system, an adaptive TSK-FCMAC, adaptive TSK-fuzzy speed observer, adaptive TSK-fuzzy rotor resistance observer, and APRO were integrated and implemented in a sensorless vector control IM drives. The robustness of the proposed adaptive TSK-FCMAC and ATSKFO against parameter variations were verified using simulations and experiments. In addition, three intelligent control schemes (i.e., an adaptive TSK-FCMAC with an improved compensating controller, adaptive TSK-FCMAC with a traditional compensating controller, and adaptive CMAC with traditional compensating controller) were assessed using experiments, and a performance index based on the root mean square error was used to evaluate each scheme by employing speeds (36-2000 rpm). The results showed that the proposed adaptive TSK-FCMAC with improved compensating controller provided substantially superior performance compared with the other schemes.