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- W4320802574 abstract "This paper investigates the problem of appointed-time neural networks based control technique for a large family of high-order non-strict-feedback nonlinear systems with input quantization. A novel appointed-time performance constraint function is designed to guarantee the tracking error converges to a small interval within the user pre-specified time. What’s more, the RBF neural networks are used to handle with the unknown sections of the system. Besides, the problem of input quantization is solved by introducing a separate lemma which converts the quantized input into a linear-like form. All the signals remain bounded, and the tracking error converges to a vere low level eventually. Finally, a simulation example proves the effectiveness of the proposed scheme." @default.
- W4320802574 created "2023-02-15" @default.
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- W4320802574 date "2022-08-15" @default.
- W4320802574 modified "2023-10-16" @default.
- W4320802574 title "A Novel Appointed-Time neural networks Control for a Class of High-Order Non-Strict-Feedback Nonlinear Systems with Input Quantization" @default.
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- W4320802574 doi "https://doi.org/10.1109/ccdc55256.2022.10033671" @default.
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