In this paper, an enhanced model-free adaptive iterative learning control (EMFAILC) method is proposed, which is applied for a class of nonlinear discrete-time systems with load disturbance and random data dropout. This method is a data-driven control strategy and only the I/O data are required for the controller design. Data are lost at every time instance and iteration instance independently, which allows successive data dropout both in time and iterative axes. By compensating the missing data, the proposed EMFAILC algorithm can track the desired time-varying trajectory. The convergence and effectiveness of the proposed approach are verified by both the rigorous mathematical analysis and the simulation results.

中文翻译：

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具有负载扰动和数据丢失的增强型无模型自适应迭代学习控制

本文提出了一种增强的无模型自适应迭代学习控制（EMFAILC）方法，该方法应用于一类具有负载扰动和随机数据丢失的非线性离散时间系统。这种方法是一种数据驱动的控制策略，控制器设计只需要 I/O 数据。数据在每个时间实例和迭代实例独立丢失，这允许在时间轴和迭代轴上连续丢失数据。通过补偿缺失的数据，所提出的 EMFAILC 算法可以跟踪所需的时变轨迹。严格的数学分析和仿真结果验证了所提出方法的收敛性和有效性。