In order to accelerate the convergence speed of iterative learning control (ILC), taking the P-type learning algorithm as an example, a correction algorithm with kernel-based autoassociative is proposed for the linear system. The learning mechanism of human brain associative memory is introduced to the traditional ILC. The control value of the subsequent time is pre-corrected with the current time information by association in each iterative learning process. The learning efficiency of the whole system is improved significantly with the proposed algorithm. Through the rigorous analysis, it shows that under this new designed ILC scheme, the uniform convergence of the state tracking error is guaranteed. Numerical simulations illustrate the effectiveness of the proposed associative control scheme and the validity of the conclusion.

中文翻译：

---

基于内核的自关联P型迭代学习控制策略

为了加快迭代学习控制（ILC）的收敛速度，以P型学习算法为例，提出了一种基于核自关联的线性系统校正算法。将人脑联想记忆的学习机制引入到传统的ILC中。后续时间的控制值在每次迭代学习过程中通过关联与当前时间信息进行预校正。提出的算法显着提高了整个系统的学习效率。通过严谨的分析表明，在这种新设计的ILC方案下，状态跟踪误差的均匀收敛是有保证的。数值模拟说明了所提出的关联控制方案的有效性和结论的有效性。