A survey on distributed iterative learning control for transient formation

• 作者: Deyuan Meng Yuxin Wu
• 来源期刊: 控制理论与技术（英文版）
• 年，卷（期）: 2021,（2）
• 所属期刊栏目: LETTER
• 页码范围: 295-297
• 页数: 3页
• 语种: 英文

摘要

Distributed control is a hot topic in many research fields.Thanks to the development of communication and computa-tion technologies,plenty of complex engineering problems can resort to the mechanism of distributed control.Of note is that most of the distributed control design only leverages the real-time information of agents,but as a consequence,only some specific steady-state objectives and/or system perfor-mances can be realized.Nonetheless,in many real scenarios,of particular importance are the transient behaviors of con-trol systems.To deal with such problems in conventional situations,iterative learning control (ILC) may provide a good alternative method that has been widely studied.ILC is one of the most popular intelligent control methodologies,which is particularly applicable for control systems running in a repetitive process over some finite time horizon of inter-est (see,e.g.,[1,2] and references therein).As a result,a classic ILC system generally evolves in the presence of two axes,i.e.,the finite time axis and the infinite iteration axis.The working mechanism of ILC is the learning from the past experiences,which enables it to improve the transient per-formances of control systems (along the time axis).Because the implementation of ILC never needs the accurate model information but only the measurement and control data,it belongs to the framework of the model-free control methods in some sense,and thus,is effective in many practical appli-cations,such as robotics,batch processes,and transportation systems.