Convergence Issue of Non-repetitive Iterative Learning Controllers for Large-scale Systems
In this paper, we embed a set of iterative learning controllers into the procedure of the steadystate optimization for a class of large-scale industrial process that consists of a number of Multiple-Input- Multiple-Output subsystems. The controllers are devised to generate a sequence of control inputs to take responsibility of a sequential step functional control signals with distinct scales. The aim of the control design is to consecutively refine the transient performance of the system. By means of Hausdor?Young inequality of involution integral, the convergence of the updating law is analyzed in the sense of Lebesguep norm. Effectiveness of the proposed control scheme is manifested by simulations.
Xiaoe Ruan Fengmin Chen Zeungnam Bien
Department of Mathematics, Faculty of Science Xian Jiaotong University Xian, P.R. China 710049 Department of Electrical Engineering and Computer Science Korea Advanced Institute of Science and Te
国际会议
青岛
英文
2006-07-21（万方平台首次上网日期，不代表论文的发表时间）