论文标题
复杂系统的交叉耦合迭代学习控制：一种单调收敛和计算有效的方法
Cross-Coupled Iterative Learning Control for Complex Systems: A Monotonically Convergent and Computationally Efficient Approach
论文作者
论文摘要
交叉耦合的迭代学习控制（ILC）可以在制造应用中实现高性能，其中跟踪轮廓对于产品的质量至关重要。本文的目的是为规范性交叉耦合ILC开发一个框架，该框架能够使用离线计算的精确轮廓误差以及迭代和时间变化的权重。开发了这种与此相关的ILC算法的单调收敛条件。此外，提出了一种资源有效的实现，其中ILC更新定律被重新构建为线性二次跟踪问题，从而大大减少了计算负载。在模拟示例中说明了该方法。
Cross-coupled iterative learning control (ILC) can achieve high performance for manufacturing applications in which tracking a contour is essential for the quality of a product. The aim of this paper is to develop a framework for norm-optimal cross-coupled ILC that enables the use of exact contour errors that are calculated offline, and iteration- and time-varying weights. Conditions for the monotonic convergence of this iteration-varying ILC algorithm are developed. In addition, a resource-efficient implementation is proposed in which the ILC update law is reframed as a linear quadratic tracking problem, reducing the computational load significantly. The approach is illustrated on a simulation example.
