Iterative Learning Control for Remote Control Systems with Communication Delay and Data Dropout
Iterative Learning Control for Remote Control Systems with Communication Delay and Data Dropout
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DOI：
10.1155/2012/705474
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发表时间：
2012-01-01
影响因子：
--
通讯作者：
Wu, Jun
中科院分区：
文献类型：
--
作者：
Liu, Chunping;Xu, Jianxin;Wu, Jun
迭代学习控制（ILC）被应用于远程控制系统中，其中从对象到控制器的通信信道受到随机数据丢失和通信延迟。通过分析表明，在数据丢失概率和通信延迟概率已知的情况下，迭代学习控制可以沿迭代轴沿着方向渐近收敛。由于基于前馈控制的本质，迭代学习控制在同时考虑数据丢失和一步延迟现象的情况下，仍能完成随机跟踪任务。理论分析和仿真验证了迭代学习控制算法在网络控制任务中的有效性。
Iterative learning control (ILC) is applied to remote control systems in which communication channels from the plant to the controller are subject to random data dropout and communication delay. Through analysis, it is shown that ILC can achieve asymptotical convergence along the iteration axis, as far as the probabilities of the data dropout and communication delay are known a priori. Owing to the essence of feedforward-based control ILC can perform trajectory-tracking tasks while both the data-dropout and the one-step delay phenomena are taken into consideration. Theoretical analysis and simulations validate the effectiveness of the ILC algorithm for network-based control tasks.