Quantization is a significant technique in network control to save limited bandwidth. In this work, two new multi-lagged-input-based quantized iterative learning control (MLI-QILC) methods are proposed by using output quantization and error quantization, respectively. The multi-lagged-input iterative dynamic linearization method (MLI-IDL) is introduced to build a linear data model of nonlinear systems using additional control inputs in lagged time instants and multiple parameters where the condition of nonzero input change is not required any longer. The MLI-QILC is proposed by designing two new objective functions utilizing the quantized data of the system outputs and tracking errors, respectively. With rigorous analysis, it is shown that the proposed MLI-QILC with output quantization guarantees that the tracking error converges to a bound which is related to the quantization density and the bound of the desired trajectory. Furthermore, an asymptotic convergence can be achieved for the proposed MLI-QILC method with error quantization. The theoretical results are verified by simulations.

中文翻译：

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多滞后输入信息增强量化迭代学习控制

量化是网络控制中用于节省有限带宽的一项重要技术。在这项工作中，分别使用输出量化和误差量化提出了两种新的基于多滞后输入的量化迭代学习控制（MLI-QILC）方法。引入了多滞后输入迭代动态线性化方法 (MLI-IDL)，在不再需要非零输入变化条件的情况下，使用滞后时刻和多个参数中的附加控制输入来构建非线性系统的线性数据模型。MLI-QILC 是通过分别利用系统输出和跟踪误差的量化数据设计两个新的目标函数而提出的。经过严谨的分析，结果表明，所提出的具有输出量化的 MLI-QILC 保证跟踪误差收敛到与量化密度和所需轨迹的界限相关的界限。此外，对于所提出的带有误差量化的 MLI-QILC 方法，可以实现渐近收敛。通过仿真验证了理论结果。