| An Adaptive Sliding Mode Tracking Controller Using BP Neural Networks for a Class of Large-scale Nonlinear Systems |
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| 作者姓名： | 刘子龙 田方 张伟军 |
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| 作者单位： | Research Inst. of Robotics Shanghai Jiaotong Univ.，Dept. of Applied Mathematics Shanghai Univ. of Finance and Economics，Research Inst. of Robotics Shanghai Jiaotong Univ.，Shanghai 200240 China，Shanghai 200135 China，Shanghai 200240 China |
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| 摘 要： | A new type controller, BP neural-networks-based sliding mode controller is developed for a class of large-scale nonlinear systems with unknown bounds of high-order interconnections in this paper. It is shown that decentralized BP neural networks are used to adaptively learn the uncertainty bounds of interconnected subsystems in the Lyapunov sense, and the outputs of the decentralized BP neural networks are then used as the parameters of the sliding mode controller to compensate for the effects of subsystems uncertainties. Using this scheme, not only strong robustness with respect to uncertainty dynamics and nonlinearities can be obtained, but also the output tracking error between the actual output of each subsystem and the corresponding desired reference output can asymptotically converge to zero. A simulation example is presented to support the validity of the proposed BP neural-networks-based sliding mode controller.
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An Adaptive Sliding Mode Tracking Controller Using BP Neural Networks for a Class of Large-scale Nonlinear Systems |
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| LIU Zi-long,TIAN Fang,ZHANG Wei-jun,.An Adaptive Sliding Mode Tracking Controller Using BP Neural Networks for a Class of Large-scale Nonlinear Systems[J].Journal of Shanghai Jiaotong university,2007,12(6):753-758. |
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| Authors: | LIU Zi-long TIAN Fang ZHANG Wei-jun |
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| Abstract: | A new type controller, BP neural-networks-based sliding mode controller is developed for a class of large-scale nonlinear systems with unknown bounds of high-order interconnections in this paper. It is shown that decentralized BP neural networks are used to adaptively learn the uncertainty bounds of interconnected subsystems in the Lyapunov sense, and the outputs of the decentralized BP neural networks are then used as the parameters of the sliding mode controller to compensate for the effects of subsystems uncertainties. Using this scheme, not only strong robustness with respect to uncertainty dynamics and nonlinearities can be obtained, but also the output tracking error between the actual output of each subsystem and the corresponding desired reference output can asymptotically converge to zero. A simulation example is presented to support the validity of the proposed BP neural-networks-based sliding mode controller. |
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| Keywords: | BP neural networks sliding mode control large-scale nonlinear systems uncertainty dynamics |
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