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RBF Neural Network-Based Sliding Mode Control for Brushless Doubly Fed Machine

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2 Author(s)
Zongkai Shao ; Sch. of Hydropower & Inf. Eng., Huazhong Univ. of Sci. & Technol., Wuhan, China ; Yuedong Zhan

In this paper, based on a radial basis function (RBF) neural network, a sliding mode control (SMC) strategy for brushless doubly fed machine (BDFM) is presented. The operating principle of BDFM has been introduced. The dynamic model of rotor field oriented and electromagnetic torque for BDFM is expressed. The proposed controller for BDFM eliminates the chattering encountered by most SMC schemes, and employs the robustness and excellent static and dynamic performances of SMC in the control system. Computer simulation results show that the proposed control strategy is of the feasibility, correctness and effectiveness.

Published in:

Control, Automation and Systems Engineering, 2009. CASE 2009. IITA International Conference on

Date of Conference:

11-12 July 2009

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