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• W2969576490 abstract "This paper proposes an innovative adaptive neural prescribed performance control (PPC) scheme for large classes of nonlinear, nonstrict-feedback systems under input saturation constraint. A restrictive hypothesis under which the upper and lower bounds of control gain functions exist a priori is first relieved by constructing appropriate compact sets within which all state trajectories are held. A novel asymmetry error transformed variable is then introduced to cope with the nondifferentiable obstacle and complex deductions corresponding to traditional PPC schemes. To efficiently manage the input saturation constraint, a new auxiliary dynamic system with a bounded compensation tangent function term is established as the strictly bounded assumption of the dynamic system is canceled. It is rigorously proven that all signals in the closed-loop systems are semiglobally uniformly ultimately bounded under both Lyapunov and invariant set theories. The tracking errors converge to a small tunable residual set with prescribed performance under the effect of the input saturation constraint. The effectiveness of the proposed control scheme is thoroughly verified by two simulation examples." @default.
• W2969576490 created "2019-08-29" @default.
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• W2969576490 date "2019-08-25" @default.
• W2969576490 modified "2023-10-17" @default.
• W2969576490 title "Approximation-Based Robust Adaptive Backstepping Prescribed Performance Control for a Huger Class of Nonlinear Systems" @default.
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• W2969576490 doi "https://doi.org/10.1155/2019/6234965" @default.
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