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| Neural Network Adaptive Sliding Mode Control for Direct-Drive Servo System |
| Yu Yongqiang, Guo Hong, Xie Zhanming |
| Beijing University of Aeronautics and Astronautics Beijing 100083 China |
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Abstract A novel neural network adaptive sliding mode control strategy is proposed, which is applied to ensure tracking capability to direct-drive-valve (DDV) servo system in the presence of plant parameter variations, cogging effect and uncertain hydraulic resistance. A radial basis function neural network (RBFNN) is utilized to realize the corrective control of sliding mode control, and compensate uncertainties of the system with adaptive learning algorithm. A conventional PD controller is designed as one parallel control part, which improves the convergence of neural network, and enhances system stability. Simulation results show that the proposed control scheme shows good tracking performance and strong robustness, and eliminates chattering effectively.
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Received: 10 July 2007
Published: 12 February 2014
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2009, Vol. 24 
