Observer Design Based on Self-Recurrent Consequent-Part Fuzzy Wavelet Neural Network
查看参考文献17篇
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文摘
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In this paper, we propose and construct an observer design based on a Self-Recurrent Consequent-Part Fuzzy Wavelet Neural Network (SRCPFWNN) for a class of nonlinear system. We use a Self-Recurrent Wavelet Neural Network (SRWNN) to construct a self-recurrent consequent part for each rule of the Takagi-Sugeno-Kang (TSK) model in the SRCPFWNN and analyze the structure of the fuzzy wavelet neural network model. Based on the Direct Adaptive Control Theory (DACT) and a back propagation-based learning algorithm, all parameters of the consequent parts are updated online in the SRCPFWNN. On this basis, we propose a design method using an adaptive state observer based on an SRCPFWNN for nonlinear systems. Using the Lyapunov function, we then prove the stability of this observer design method. Our simulation results confirm that the observer can accurately and quickly estimate the state values of the system. |
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来源
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Tsinghua Science and Technology
,2016,21(5):544-551 【核心库】
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DOI
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10.1109/tst.2016.7590323
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关键词
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Takagi-Sugeno-Kang (TSK) fuzzy model
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activation functions
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state observer
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nonlinear systems
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simulation
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地址
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1.
Department of Aeronautics and Astronautics, Shenyang Aerospace University, Shenyang, 110136
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School Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016
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语种
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英文 |
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文献类型
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研究性论文 |
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ISSN
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1007-0214 |
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学科
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自动化技术、计算机技术 |
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文献收藏号
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CSCD:5827683
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17
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