Meng Deyuan, Zhang Jingyao
IEEE Trans Neural Netw Learn Syst. 2018 Aug;29(8):3885-3892. doi: 10.1109/TNNLS.2017.2734843. Epub 2017 Aug 29.
This brief addresses the iterative learning control (ILC) problems for discrete-time systems subject to iteration-dependent tracking time intervals. A modified class of P-type ILC algorithms is proposed by properly defining an available modified output, for which robust convergence analysis is performed with an inductive approach. It is shown that if a persistent full-learning property is ensured, then a necessary and sufficient convergence condition of ILC can be derived to reach the perfect output tracking objective though the tracking time interval is iteration-dependent. That is, the tracking of ILC for iteration-dependent time intervals can be guaranteed in the same deterministic (not stochastic) convergence way as that of traditional ILC over a fixed time interval. Furthermore, the developed tracking results can be extended to admit iteration-dependent uncertainties in initial state and external disturbances. Simulation tests are also included to demonstrate the effectiveness of the modified P-type ILC.
本文探讨了受迭代相关跟踪时间间隔影响的离散时间系统的迭代学习控制（ILC）问题。通过适当定义一个可用的修正输出，提出了一类改进的P型ILC算法，并采用归纳法对其进行了鲁棒收敛性分析。结果表明，如果确保了持续完全学习特性，那么尽管跟踪时间间隔与迭代有关，但仍可推导出ILC达到完美输出跟踪目标的充要收敛条件。也就是说，对于与迭代相关的时间间隔，ILC的跟踪可以通过与传统ILC在固定时间间隔内相同的确定性（非随机）收敛方式得到保证。此外，所得到的跟踪结果可以扩展到允许初始状态和外部干扰中存在与迭代相关的不确定性。还包括仿真测试以证明改进的P型ILC的有效性。
