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- W4379878901 abstract "In this study, a prescribed performance adaptive neural output feedback controller is developed for full-state-constrained nonlinear systems that are subject to uncertainties and input saturation. To approximate system unknown nonlinear functions, the radial basis function neural network is employed. And, based on the approximated outputs of neural network, the state observer and nonlinear disturbance observer are structured to evaluate unmeasured states and unknown lumped disturbances, respectively. Barrier Lyapunov function involving prescribed performance function is constructed to constrain tracking error and all states within the specified ranges. Then, based on the constructed neural network and observers, an output feedback controller is proposed to constrain the tracking error and states via backstepping technique. Moreover, an auxiliary dynamic system is established to deal with input saturation possible caused by the more efforts to constrain tracking error and states under rough working conditions. Moreover, stability of the entire closed-loop system is strictly certified using Lyapunov theory, and the desirable prescribed tracking performance without violation of all states is assured under the combined influence of uncertainties and input saturation. Finally, the proposed control method is verified by two examples." @default.
- W4379878901 created "2023-06-09" @default.
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- W4379878901 date "2023-08-01" @default.
- W4379878901 modified "2023-10-18" @default.
- W4379878901 title "Observer-based prescribed performance adaptive neural output feedback control for full-state-constrained nonlinear systems with input saturation" @default.
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- W4379878901 doi "https://doi.org/10.1016/j.chaos.2023.113593" @default.
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