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.
[1] Chung S K, Lee J H, Ko J S, et al. A robust speed control of brushless direct drive motor using integral variable structure control with sliding mode observer[C]. IEEE Industry Applications Society Annual Meeting, Denver, 1994. [2] Young K D, Utkin V I, Ozguner U. A control engineer’s guide to sliding mode control[J]. IEEE Transactions on Control Systems Technology, 1999, 7(3): 328-342. [3] Tan Y K, Panda S K. Sliding-mode position controller for linear permanent magnet brushless DC servo motors[C]. International Conference on Power Electronics and Drives Systems, 2003. [4] 史晓娟. 基于复合滑模变结构控制的位置伺服系统的研究[J]. 电工技术学报, 2003, 18(3): 64-66. [5] 瞿少成, 王永骥. BLDC位置伺服系统的离散变结构控制[J]. 中国电机工程学报, 2004, 24(6): 96-99. [6] 孙宜标, 郭庆鼎. 基于RBF神经网络补偿的直线伺服系统滑模鲁棒跟踪控制[J]. 控制理论与应用, 2004, 21(2): 252-256. [7] Huang S J, Huang K S, Chiou K C. Development and application of a novel radial basis function sliding mode controller[J]. Mechatronics, 2003, 13: 313-329. [8] Sarwer M G, Rafiq Md A, Datta M, et al. Chattering free neuro-sliding mode control of DC drives[C]. International Conference on Power Electronics and Drives Systems, 2005. [9] Ak A G, Cansever G. Three link robot control with fuzzy sliding mode controller based on RBF neural network[C]. IEEE International Symposium on Intelligent Control, Munich, 2006. [10] Tsai C H, Chung H Y, Yu F M. Neuro-sliding mode control with its applications to seesaw systems[J]. IEEE Transactions on Neural Networks, 2004, 15(1): 124-134.
2009, Vol. 24 
