Pseudo-downsampled iterative learning control
Bin Zhang
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore
Search for more papers by this authorCorresponding Author
Danwei Wang
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore===Search for more papers by this authorKeliang Zhou
School of Electrical Engineering, Southeast University, Nanjing 210096, China
Search for more papers by this authorYongqiang Ye
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore
Search for more papers by this authorYigang Wang
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore
Search for more papers by this authorBin Zhang
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore
Search for more papers by this authorCorresponding Author
Danwei Wang
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore===Search for more papers by this authorKeliang Zhou
School of Electrical Engineering, Southeast University, Nanjing 210096, China
Search for more papers by this authorYongqiang Ye
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore
Search for more papers by this authorYigang Wang
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore
Search for more papers by this authorAbstract
In this paper, a simple and effective multirate iterative learning control (ILC), referred as pseudo-downsampled ILC, is proposed to deal with initial state error. This scheme downsamples the tracking error and input signals collected from the feedback control system before they are used in the ILC learning law. The output of the ILC is interpolated to generate the input for the next cycle. Analysis shows that the exponential decay of the tracking error can be expected and convergence condition can be ensured by downsampling. Other advantages of the proposed pseudo-downsampled ILC include no need for a filter design and reduction of memory size and computation. Experimental results demonstrate the effectiveness of the proposed scheme. Copyright © 2007 John Wiley & Sons, Ltd.
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