Design and implementation sliding mode controller based on radial basis function neural network for synchronous reluctance motor

Author

Chen, Chien-An ; Lin, Wen-Bin ; Chiang, Huann-Keng

Author_Institution

Grad. Sch. of Eng. Sci. & Technolog, Nat. Yunlin Univ. of Sci. & Technol., Douliu

fYear

2009

fDate

25-27 May 2009

Firstpage

281

Lastpage

286

Abstract

This paper presents a sliding mode control (SMC) design based on radial basis function neural network (RBFNN) to robust stabilization and disturbance rejection of the synchronous reluctance motor (SynRM) drive system. This method utilizes Lyapunov function and the steep descent rule to guarantee the convergence of the SynRM system asymptotically. Finally, we employ the experiments to validate the proposed method.

Keywords

Lyapunov methods; control system synthesis; convergence; neurocontrollers; radial basis function networks; reluctance motor drives; robust control; variable structure systems; Lyapunov function; convergence; disturbance rejection; radial basis function neural network; robust stabilization; sliding mode control design; steep descent rule; synchronous reluctance motor drive system; Control systems; Feedforward neural networks; Mathematical model; Neural networks; Radial basis function networks; Reluctance motors; Robust control; Sliding mode control; Synchronous motors; Uncertainty; Lyapunov function; Radial Basis Function Neural Network; Sliding Mode Control; steep descent rule; synchronous reluctance motor;

fLanguage

English

Publisher

ieee

Conference_Titel

Industrial Electronics and Applications, 2009. ICIEA 2009. 4th IEEE Conference on

Conference_Location

Xi´an

Print_ISBN

978-1-4244-2799-4

Electronic_ISBN

978-1-4244-2800-7

Type

conf

DOI

10.1109/ICIEA.2009.5138206

Filename

5138206