西安工业大学“知行讲坛”
讲坛题目：迭代学习控制系统输出能控性、输出学习能力及动力学的可学习性
主讲人：刘 健博士
讲座时间：2021年11月25日，10:40-11:40
讲座地点：工5-404
主办单位：研究生院
承办单位：电子信息工程学院
摘要：This article considers iterative learning control (ILC) for a class of discrete-time systems with full learnability and unknown system dynamics. First, we give a framework to analyze the learnability of the control system and build the relationship between the learnability of the control system and the input–output coupling matrix (IOCM). The control system has full learnability if and only if the IOCM is full-row rank and the control system has no learnability almost everywhere if and only if the rank of the IOCM is less than the dimension of system output. Second, by using the repetitiveness of the control system, some data-based learning schemes are developed. It is shown that we can obtain all the needed information on system dynamics through the developed learning schemes if the control system is controllable. Third, by the dynamic characteristics of system outputs of the ILC system along the iteration direction, we show how to use the available information of system dynamics to design the iterative learning gain matrix and the current state feedback gain matrix. And we strictly prove that the iterative learning scheme with the current state feedback mechanism can guarantee the monotone convergence of the ILC process if the IOCMis full-row rank. Finally, a numerical example is provided to validate the effectiveness of the proposed iterative learning scheme with the current state feedback mechanism.
个人简介：
刘健，2017年获西安交通大学应用数学专业博士学位，2010年获东华大学基础数学专业硕士学位，2007年获阜阳大学数学与应用数学专业学士学位。自2017年以来，就职于西安电子科技大学机电工程学院。研究兴趣包括迭代学习控制、网络控制系统和强化学习，在国内外学术期刊发表论文二十余篇，主持国家自然科学基金青年项目一项、陕西省自然科学基金青年项目一项。
