This work presents a quantisation compensation-based data-driven iterative learning control (QC-DDILC) scheme by incorporating a uniform quantiser and an encoding–decoding mechanism (E-DM) to deal with the problem of limited communication resources in a networked control system. Since it is directly aimed at a nonlinear nonaffine system, an iterative dynamic linearisation method is employed to transfer it to a linear data model. Then, the QC-DDILC method is developed by the use of optimisation technique for the learning control law and the parameter updating law, respectively, where the quantised output from the E-DM is used. Since the direct output measurement of the system is unavailable, the linear data model is also acted as an iterative predictive model to estimate the system outputs utilised as the compensator in the consequent QC-DDILC. The proposed QC-DDILC is a data-driven method without relying on any explicit mechanism model information. The convergence analysis is conducted by using the mathematical tools of contraction mapping and induction. Simulations verify the theoretical results.

这项工作提出了一种基于量化补偿的数据驱动迭代学习控制 (QC-DDILC) 方案，通过结合统一量化器和编码 - 解码机制 (E-DM) 来解决网络控制系统中通信资源有限的问题. 由于它直接针对非线性非仿射系统，因此采用迭代动态线性化方法将其转换为线性数据模型。然后，QC-DDILC 方法分别通过使用学习控制律和参数更新律的优化技术开发，其中使用了 E-DM 的量化输出。由于系统的直接输出测量不可用，因此线性数据模型也用作迭代预测模型，以估计在随后的 QC-DDILC 中用作补偿器的系统输出。所提出的 QC-DDILC 是一种数据驱动的方法，不依赖于任何显式机制模型信息。通过使用收缩映射和归纳的数学工具进行收敛分析。仿真验证了理论结果。