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Adaptive Neural Control of SISO Non-Affine Nonlinear Time-Delay Systems with Unknown Hysteresis Input

机译:具有未知滞后输入的Siso非仿射非线性时滞系统的自适应神经控制

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In this paper, adaptive neural control is investigated for a class of SISO unknown non-affine nonlinear systems with state time-varying delays and unknown hysteresis input. The non-affine problem is solved by adopting mean value theorem and implicit function theorem. The unknown time-varying delay uncertainties are compensated for using appropriate Lyapunov-Krasovskii functionals in the design. The effect of the unknown hysteresis with the Prandtl-Ishlinskii model is also mitigated through the proposed adaptive control. By utilizing the Lyapunov synthesis, the closed-loop control system is proved to be semi-globally uniformly ultimately bounded (SGUUB).
机译:本文研究了一种具有状态时变延迟和未知滞后输入的SISO未知非仿射非线性系统的自适应神经控制。通过采用均值定理和隐式功能定理来解决非仿射问题。在设计中使用适当的Lyapunov-Krasovskii函数来补偿未知的时变延迟不确定性。通过所提出的自适应控制,还减轻了未知滞后与Prandtl-Ishlinskii模型的影响。通过利用Lyapunov合成,证明闭环控制系统被证明是半全球均匀的最终有界(Sgub)。

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