The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON CIRCUITS AND SYSTEMS
Transactions ID Number: 27-641
Full Name: Chun-Fei Hsu
Position: Assistant Professor
Age: ON
Sex: Male
Address: Department of Electrical Engineering, Chung Hua University, Hsinchu 300, Taiwan
Country: TAIWAN
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E-mail address: fei@chu.edu.tw
Other E-mails: s861730@mail.yzu.edu.tw
Title of the Paper: FPGA-based real-time implementation of an adaptive RCMAC control system
Authors as they appear in the Paper: Chih-Min Lin; Chao-Ming Chung; Chun-Fei Hsu
Email addresses of all the authors: cml@saturn.yzu.edu.tw; s948507@mail.yzu.edu.tw; fei@chu.edu.tw
Number of paper pages: 10
Abstract: The main advantage of the recurrent cerebellar model articulation controller (RCMAC) is its rapid learning rate compared to other neural networks. This paper proposes an adaptive RCMAC control system for a brushless DC (BLDC) motor. The proposed control scheme is composed of an RCMAC controller and a compensation controller. The RCMAC controller is used to mimic an ideal controller, and the compensation controller is designed to compensate for the approximation error between the ideal controller and the RCMAC controller. The Lyapunov stability theory is utilized to derive the parameter tuning algorithm, so that the uniformly ultimately bound stability of the closed-loop system can be achieved. As compared with standard adaptive controller, the proposed control scheme does not require persistent excitation condition. Then, the developed adaptive RCMAC control system is implemented on a field programmable gate array (FPGA) chip for controlling a brushless DC motor. E!
xperimental results reveal that the proposed adaptive RCMAC control system can achieve favorable tracking performance. Since the developed adaptive RCMAC control system uses a hyperbolic tangent function to compensate for the approximation error, there is no chattering phenomenon in the control effort. Thus, the proposed control system is more suitable for real-time practical control applications.
Keywords: BLDC; FPGA implementation; RCMAC; adaptive control; Lyapunov function; neural control; uniformly ultimately bound stability.
EXTENSION of the file: .pdf
Special (Invited) Session: FPGA-implemented adaptive RCMAC design for BLDC motors
Organizer of the Session: 591-838
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