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- W4387123574 abstract "In this paper, the finite-time adaptive control issue is addressed for a class of switched strict-feedback nonlinear systems with input quantization and output constraints. Firstly, the barrier Lyapunov function is introduced to cope with statistic output constraints, and with the help of Young's inequality, only one learning parameter of neural networks needs to be tuned online at each step of recursion. Then, a hysteresis quantizer is introduced to handle the input signal. Finally, by appropriately choosing the initial value of the switched subsystem and using the characteristics of dynamic surface control, finite-time adaptive neural quantized control is designed to guarantee that all signals are semi-globally practical finite-time stable. Simulation results verify the effectiveness of the proposed means." @default.
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- W4387123574 date "2023-07-11" @default.
- W4387123574 modified "2023-10-18" @default.
- W4387123574 title "Finite-Time Adaptive Quantized Control of Switched Constrained Nonlinear Systems" @default.
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- W4387123574 doi "https://doi.org/10.1109/cyber59472.2023.10256449" @default.
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