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- W2393035823 abstract "The adaptive neural-network dynamic surface-control is presented for a class of nonlinear systems in a strictfeedback form with unmodeled dynamics.This scheme extends the routine method of dynamic surface-control to the strict-feedback nonlinear systems with unmodeled dynamics,thus broadens the application of the dynamic surface-control. The effects of unmodeled dynamics are reduced by introducing the defined compact set to dynamic surface-control design. Using Young's inequality,we developed two adaptive parameter-tuning schemes.Compared with the existing results,the proposed approach reduces the number of adjustable parameters effectively,relaxes constrain on dynamic uncertainties and eliminates the requirements of the derivatives of virtual control coefficients.By theoretical analysis,the closed-loop control system is shown to be semi-globally uniformly ultimately bounded,with the tracking error converging to a small neighborhood of the origin." @default.
- W2393035823 created "2016-06-24" @default.
- W2393035823 creator A5007015049 @default.
- W2393035823 date "2013-01-01" @default.
- W2393035823 modified "2023-09-26" @default.
- W2393035823 title "Adaptive neural-network dynamic surface-control with unmodeled dynamics" @default.
- W2393035823 hasPublicationYear "2013" @default.
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