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针对迭代学习P型控制算法对初始偏差和输出误差扰动的敏感性问题,研究了一种带有遗忘因子的时变非线性系统的迭代学习控制方法。在有扰动的情况下,利用迭代学习过程记忆的期望轨迹,期望控制以及跟踪误差,通过有界学习增益和批次时变因子设计学习控制器,并基于算子理论给出了控制算法存在的充分必要条件及其收敛性分析,改善了系统的鲁棒性和动态特性。最后以注塑机的注射速度控制仿真验证了本文算法的有效性。
Aiming at the sensitivity of iterative learning P-type control algorithm to initial bias and output error disturbance, an iterative learning control method for a class of time-varying nonlinear systems with forgetting factor is studied. In the case of disturbance, the learning trajectory of the iterative learning process memory, the expected control and the tracking error are used to design the learning controller through the bounded learning gain and the batch time-varying factor. The control algorithm is given based on the operator theory The necessary and sufficient conditions and their convergence analysis are improved to improve the system robustness and dynamic characteristics. Finally, the injection speed control of the injection molding machine simulation shows the effectiveness of the proposed algorithm.