期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 48, 期 2, 页码 730-741出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2017.2655053
关键词
Composite intelligent learning; disturbance observer; fuzzy logic system (FLS); neural networks (NNs); serial-parallel estimation model (SPEM); strict-feedback form
类别
资金
- National Natural Science Foundation of China [61622308]
- Aeronautical Science Foundation of China [2015ZA53003]
- Natural Science Basic Research Plan in Shaanxi Province [2016KJXX-86]
- Fundamental Research Funds of Shenzhen Science and Technology Project [JCYJ20160229172341417]
This paper addresses the dynamic surface control of uncertain nonlinear systems on the basis of composite intelligent learning and disturbance observer in presence of unknown system nonlinearity and time-varying disturbance. The serial-parallel estimation model with intelligent approximation and disturbance estimation is built to obtain the prediction error and in this way the composite law for weights updating is constructed. The nonlinear disturbance observer is developed using intelligent approximation information while the disturbance estimation is guaranteed to converge to a bounded compact set. The highlight is that different from previous work directly toward asymptotic stability, the transparency of the intelligent approximation and disturbance estimation is included in the control scheme. The uniformly ultimate boundedness stability is analyzed via Lyapunov method. Through simulation verification, the composite intelligent learning with disturbance observer can efficiently estimate the effect caused by system nonlinearity and disturbance while the proposed approach obtains better performance with higher accuracy.
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