Multi-lagged-input iterative dynamic linearization based data-driven adaptive iterative learning control |
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| Authors: | Na Lin Ronghu Chi Biao Huang Zhongsheng Hou |
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| Institution: | 1. College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao 266061, PR China;2. Institute of Artificial Intelligent and Control, Qingdao University of Science and Technology, Qingdao 266061, PR China;3. Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB T6G 2G6, Canada;4. Advanced Control Systems Lab, School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing 100044, PR China |
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| Abstract: | This article presents a multi-lagged-input based data-driven adaptive iterative learning control (M-DDAILC) method for nonlinear multiple-input-multiple-output (MIMO) systems by virtue of multi-lagged-input iterative dynamic linearization (IDL). The original nonlinear and non-affine MIMO system is equivalently transformed into a linear input-output incremental counterpart without loss of dynamics. The proposed learning law utilizes the desired trajectory to cancel the influence from iteration-by-iteration variations, as well as additional multi-lagged inputs to improve control performance. The developed iterative estimation law is more effective and also makes estimation of the unknown parameters easier because the dynamics for each parameter to represent are decreased by dividing the system into multiple components in the multi-lagged-input IDL formulation. Moreover, the proposed M-DDAILC does not need an explicit and accurate model. It is proved to be iteratively convergent with rigorous analysis. Both a numerical example and a practical application to a permanent magnet linear motor are provided to verify the validity and applicability of the proposed method. |
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| Keywords: | Corresponding author at: College of Automation and Electronic Engineering Qingdao University of Science and Technology Qingdao 266061 PR China |
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