高速电主轴块控自适应反演控制

PDF(1665 KB)

振动与冲击 ›› 2015, Vol. 34 ›› Issue (23) : 99-105.

论文

高速电主轴块控自适应反演控制

单文桃1，陈小安2

作者信息 +

Block adaptive backstepping control for high-speed motorized spindle

Wen-tao Shan1, Xiao-an Chen2

Author information +

文章历史 +

摘要

针对高速电主轴驱动控制系统的不确定性，强耦合，非线性等难点，提出基于智能RBF调节神经网络的高速电主轴块控自适应反演控制方案。采用李雅普诺夫稳定性理论推导出主轴系统的控制律和参数自适应律，主轴转子转速及磁链跟踪显示出优良的瞬态性能并且能够对附加扭矩干扰以及主轴系统的不确定性具有较强的鲁棒性。仿真结果验证了此方案的有效性和合理性。

Abstract

According to high-speed motorized spindle (HSMS) is a complex controlled object of uncertainty, nonlinear and strong coupling, this paper proposes a block adaptive backstepping control for it based on global RBF neural network. Backstepping control laws and parameter updating laws are derived using Lyapunov theory, which guarantees the stability of the whole spindle system. With the proposed block adaptive backstepping controller, the rotor speed and flux reference signals tracking of the high-speed motorized spindle possesses the advantages of nice transient control performance and robustness to the load torque disturbance and parametric uncertainty. Simulation results validate the effectiveness and rationality of the proposed control scheme.

导出引用

单文桃1，陈小安2. 高速电主轴块控自适应反演控制[J]. 振动与冲击, 2015, 34(23): 99-105

Wen-tao Shan1, Xiao-an Chen2. Block adaptive backstepping control for high-speed motorized spindle[J]. Journal of Vibration and Shock, 2015, 34(23): 99-105

参考文献

[1] Polycarpou, M.M.. Stable adaptive neural control scheme for nonlinear systems[J]. IEEE Transactions on Automatic Control, 2006, 41(3)：447－451.
[2] Chiman Kwan, Lewis, F.L., Dawson, D.M.. Robust neural-network control of rigid-link electrically driven robots[J]. IEEE Transactions on Neural Networks,1998. 9(4)：581－588.
[3] S.S. GE, C. WANG,T. H. LEE. Adaptive backstepping control of a class of chaotic systems[J],International Journal of Bifurcation and Chaos,2000,10(5)：1149－1157.
[4] T. Zhang, S.S. Ge,C.C. Hang. Adaptive neural network control for strict-feedback nonlinear systems using backstepping design[J]. Automatica,2000. 36(12): 1835－1846.
[5] L Karsenti,F Lamnabhi-Lagarrigue,G Bastin. Backstepping technique extended to nonlinear parameterization[J]. System and Control Letters. 1996. San Francisco,USA.
[6] T Knohl, H Unbehauen.ANNNAC extension of adaptive backstepping algorithm with artificial neural networks[J]. IEEE Proceedings Control Theory and Applications. 2000,174(2): 177－183.
[7] Hua Deng, Han-Xiong Li, Yi-hu Wu. Feedback-Linearization-Based Neural Adaptive Control for Unknown Nonaffine Nonlinear Discrete-Time Systems[J]. IEEE Transactions on Neural Networks,2008. 19(9)：1615－1625.
[8] Ge,S.S. Chenguang Yang,Tong Heng Lee. Adaptive Predictive Control Using Neural Network for a Class of Pure-Feedback Systems in Discrete Time[J]. IEEE Transactions on Neural Networks,2008. 19(9)：1599－1614.
[9] Hongbin Du,H.S.. Pingjing Yao,Adaptive neural network for a class of low-Triangular-Structured Nonlinear Systems[J]. IEEE Transactions on Neural Networks,2006. 17(2)：509－514.
[10]    单文桃. 高速电主轴驱动性能及智能控制研究[博士学位论文]. 重庆：重庆大学,2014.
[11]    单文桃，陈小安，合烨，周明红，刘俊峰. 基于免疫遗传算法的模糊径向基函数神经网络在高速电主轴中的应用[J]. 机械工程学报，2013, 49(23)：167－173.
       Shan Wen-tao, Chen Xiao-an, He Ye, Zhou Minghong, Liu Jun-feng. Application of Immune Genetic Algorithm Based Fuzzy RBF Neural Network in High-speed Motorized Spindles[J]. Journal of mechanical engineering, 2013,49(23): 167-173.
[12] 孟杰. 高速电主轴动力学分析与实验研究[博士学位论文]. 重庆：重庆大学,2008.
[13] Polycarpou, M.M, Loannou P A. A robust adaptive nonlinear control design[J]. Automatica,1996,32(3)：423－427.
[14] 胡云安，晋玉强，李海燕著. 非线性系统鲁棒自适应反演控制[M]. 北京：电子工业出版社,2010.