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A data-driven adaptive iterative learning predictive control for a class of discrete-time nonlinear systems

机译:一类离散时间非线性系统的数据驱动自适应迭代学习预测控制

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On the basis of dynamic linearization method along the iteration axis, a novel data-driven adaptive iterative learning predictive control (AILPC) is presented for a class of general repeatable discrete-time nonlinear systems. The highlight of the algorithm is that the controller design only depends on the I/O data of the dynamical system without using any priori knowledge of the system. The monotonic convergence and effectiveness of the AILPC algorithm are proven and verified through rigorous analyses, numerical example and freeway traffic flow control application.
机译:基于沿着迭代轴的动态线性化方法,针对一类通用的可重复离散时间非线性系统,提出了一种新颖的数据驱动的自适应迭代学习预测控制(AILPC)。该算法的亮点在于,控制器设计仅取决于动态系统的I / O数据,而无需使用系统的任何先验知识。通过严格的分析,数值算例和高速公路交通流量控制应用,证明和验证了AILPC算法的单调收敛性和有效性。

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