Abstract Adaptive iterative learning control of a class of nonlinear time-delay systems with unknown backlash-like hysteresis input and control direction
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Adaptive iterative learning control of a class of nonlinear time-delay systems with unknown backlash-like hysteresis input and control direction

机译:一种非线性时滞系统的自适应迭代学习控制,具有未知的间隙滞后输入和控制方向

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Abstract This paper presents an adaptive iterative learning control scheme for a class of nonlinear systems with unknown time-varying delays and control direction preceded by unknown nonlinear backlash-like hysteresis. Boundary layer function is introduced to construct an auxiliary error variable, which relaxes the identical initial condition assumption of iterative learning control. For the controller design, integral Lyapunov function candidate is used, which avoids the possible singularity problem by introducing hyperbolic tangent funciton. After compensating for uncertainties with time-varying delays by combining appropriate Lyapunov-Krasovskii function with Young's inequality, an adaptive iterative learning control scheme is designed through neural approximation technique and Nussbaum function method. On the basis of the hyperbolic tangent function's characteristics, the system output is proved to converge to a small neighborhood of the desired trajectory by constructing Lyapunov-like composite energy function (CEF) in two cases, while keeping all the closed-loop signals bounded. Finally, a simulation example is presented to verify the effectiveness of the proposed approach. ]]>
机译:<![cdata [ 抽象 本文提出了一类非线性系统的自适应迭代学习控制方案,其中一个非线性系统具有未知的时变延迟和控制方向,前面由未知的非线性反弹 - 像滞后一样。引入边界层函数来构造辅助误差变量,其放松迭代学习控制的相同初始条件假设。对于控制器设计,使用积分Lyapunov功能候选者,通过引入双曲线切线磁性函数来避免可能的奇点问题。通过将适当的Lyapunov-Krasovskii功能与杨氏不等式相结合,通过神经逼近技术和NUSSBAUM函数方法来补偿具有相应Lyapunov-Krasovskii功能的不确定延迟的不确定性之后。在双曲线切线函数的特性的基础上,证明系统输出通过在两种情况下构造Lyapunov样式能量函数(CEF)来汇聚到所需轨迹的小邻域,同时保持所有闭环信号界面。最后,提出了一种仿真示例以验证所提出的方法的有效性。 ]]>

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