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- W2783221700 abstract "In this work, an adaptive dynamic surface control scheme is studied for a class of nonlinear systems with unknown functions and unknown non-symmetric dead-zone nonlinearity. The unknown asymmetric dead-zone is described as a combination of a linear term and a disturbance-like term. Radial basis function neural networks (RBFNNs) are used in the online approximation of unknown functions and disturbance-like term of the dead-zone model and adaptive laws are designed to adjust the weights of network. Using the RBFNN-based model, the dead-zone model and the dynamic surface control (DSC) technique, the adaptive control scheme is developed for uncertain nonlinear systems with dead-zone nonlinearity. The proposed scheme eliminates the ‘explosion of complexity’ problem and presents a singular-free adaptive DSC control scheme. Also, it does not require any knowledge about unknown terms and the dead-zone nonlinearity. Simulation results are provided to demonstrate the performance and effectiveness of the proposed approach." @default.
- W2783221700 created "2018-01-26" @default.
- W2783221700 creator A5082259055 @default.
- W2783221700 date "2017-11-01" @default.
- W2783221700 modified "2023-09-25" @default.
- W2783221700 title "Radial basis function neural network-based control for uncertain nonlinear systems with unknown dead-zone input" @default.
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- W2783221700 doi "https://doi.org/10.1109/icciautom.2017.8258644" @default.
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