Adaptive Optimal Integral Sliding Mode Control for a Dual-motor Driving Servo System
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- 英文论文题名：Adaptive Optimal Integral Sliding Mode Control for a Dual-motor Driving Servo System
- 论文作者：Minlin Wang ; Xuemei Ren ; Linwei Li ; Qiang Chen
- 英文论文作者：Minlin Wang ; Xuemei Ren ; Linwei Li ; Qiang Chen ; School of Automation ; Beijing Institute of Technology ; College of Information Engineering ; Zhejiang University of Technology
- 年：2017
- 作者机构：School of Automation,Beijing Institute of Technology;College of Information Engineering,Zhejiang University of Technology;
- 英文论文关键词：Multi-motor driving servo system ; neural network state observer ; integral sliding mode control ; optimal control
- 会议召开时间：2017-07-26
- 会议录名称：第36届中国控制会议论文集（A）
- 英文会议录名称：Proceedings of the 36th Chinese Control Conference（A）
- 语种：英文
- 分类号：TM921.54;TP273
- 学会代码：KZLL
- 会议名称：第36届中国控制会议
- 会议地点：中国辽宁大连
- 主办单位：中国自动化学会控制理论专业委员会
- 学会名称：中国自动化学会控制理论专业委员会
- 页数：6
- 文件大小：346k
- 原文格式：D
- 会议级别：全国
摘要
In this paper, an adaptive optimal integral sliding mode controller is presented to realize the load position tracking for a multi-motor driving servo system(MDSS). By reformulating the MDSS in a strict feedback form, a neural network state observer is designed to estimate both immeasurable states and unknown nonlinearities. Based on this state observer, two adaptive optimal integral sliding mode controllers are developed and integrated into one tracking controller through the backstepping approach.The proposed controllers can not only guarantee a satisfactory load tracking performance but also increase the robustness to system uncertainties. By using the Lyapunov stability theorem, it is certified that all signals of the MDSS are uniformly ultimately bounded. Simulation results on a four-motor driving servo system are conducted to validate the efficiency of the proposed control scheme.
In this paper, an adaptive optimal integral sliding mode controller is presented to realize the load position tracking for a multi-motor driving servo system(MDSS). By reformulating the MDSS in a strict feedback form, a neural network state observer is designed to estimate both immeasurable states and unknown nonlinearities. Based on this state observer, two adaptive optimal integral sliding mode controllers are developed and integrated into one tracking controller through the backstepping approach.The proposed controllers can not only guarantee a satisfactory load tracking performance but also increase the robustness to system uncertainties. By using the Lyapunov stability theorem, it is certified that all signals of the MDSS are uniformly ultimately bounded. Simulation results on a four-motor driving servo system are conducted to validate the efficiency of the proposed control scheme.
In this paper, an adaptive optimal integral sliding mode controller is presented to realize the load position tracking for a multi-motor driving servo system(MDSS). By reformulating the MDSS in a strict feedback form, a neural network state observer is designed to estimate both immeasurable states and unknown nonlinearities. Based on this state observer, two adaptive optimal integral sliding mode controllers are developed and integrated into one tracking controller through the backstepping approach.The proposed controllers can not only guarantee a satisfactory load tracking performance but also increase the robustness to system uncertainties. By using the Lyapunov stability theorem, it is certified that all signals of the MDSS are uniformly ultimately bounded. Simulation results on a four-motor driving servo system are conducted to validate the efficiency of the proposed control scheme.
引文
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[2]T.Gang and M.Xiaoli and L.Yi,Optimal and nonlinear decoupling control of systems with sandwiched backlash,Automatica,37(2):165–176,2001.
[3]W.Zhao,X.Ren,X.Gao,Synchronization and tracking control for multi-motor driving servo systems with backlash and friction,International Journal of Robust&Nonlinear Control,26(13):2745–2766,2015.
[4]X.Ren,D.Li,G.Sun and W.Zhao,Eso-Based Adaptive Robust Control of Dual Motor Driving Servo System,Asian Journal of Control,18(6):2358-2365,2014.
[5]G.Sun,X.Ren and D.Li,Neural Active Disturbance Rejection Output Control of Multimotor Servomechanism,IEEE Transactions on Control Systems Technology,23(2):746-753,2015.
[6]M.Wang,X.Ren,Q.Chen and et al.Modified dynamic surface approach with bias torque for multi-motor servomechanism,Control Engineering Practice,50:57-68,2016.
[7]F.L.Lewis,and D.L.Vrabie and V.L.Syrmos,Optimal Control of Continuous-Time Systems,John Wiley&Sons,Inc.,110–176,2012.
[8]A.Mannava.and S.N.Balakrishnan and L.Tang and R.G.Landers,Optimal Tracking Control of Motion Systems IEEE Transactions on Control Systems Technology,20(20):1548–1558,2012
[9]Y.H.Kim and F.L.Lewis,Optimal design of CMAC neuralnetwork controller for robot manipulators,IEEE Transactions on Systems Man&Cybernetics Part C,30(1):22–31,2000.
[10]W.Jianan,Integrated Optimal Formation Control of Multiple Unmanned Aerial Vehicles,IEEE Transactions on Control Systems Technology,21(5):1731–1744,2013.
[11]A.Levant,Higher-order sliding modes,differentiation and output-feedback control,International Journal of Control,76(9-10):924–941,2003.