Data-driven ILC algorithms using AFD in frequency domain for unknown linear discrete-time systems |
| |
| Institution: | 1. School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou 510006, China;2. College of Information Science and Engineering, Huaqiao Univesity, Xiamen 361002, China;3. School of Information Science, Guangzhou Xinhua University, Dongguan 523133, China;4. Marcau Centre for Mathematical Sciences, Macau University of Science and Technology, Macau, China;1. School of Mathematics and Statistics, Beijing Institute of Technology, Beijing 100081, China;2. School of Automation, Beijing Institute of Technology, Beijing 100081, China;3. School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China;1. Centro Universitario de la Ciénega, Universidad de Guadalajara Av. Universidad No. 1115, Col. Lindavista, Ocotlán 47820, Jalisco, México;2. Department of Information Engineering, Computer Science and Mathematics, University of L’Aquila, Via Vetoio, Loc. Coppito, L’Aquila 67100, Italy;3. Center of Excellence DEWS, University of L’Aquila Via Vetoio, Loc. Coppito, L’Aquila 67100, Italy;4. Quartz Laboratory EA 7393, ENSEA 6 Avenue du Ponçeau, Cergy Pontoise Cedex 95014, France;5. LS2N UMR 6004 CNRS, École Centrale Nantes France;1. School of Computer and Control Engineering, Yantai University, Yantai, Shandong 264005, China;2. Department of Mathematics, Harbin Institute of Technology, Weihai 264209, China;3. Department of Electrical Engineering, Yeungnam University, 280 Daehak-Ro, Kyongsan 38541, Republic of Korea;4. School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China;5. School of Mathematics and Information Sciences, Weifang University, Weifang, Shandong 261061, China;1. Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China;2. Key Laboratory of Advanced Manufacture Technology for Automobile Parts, Chongqing University of Technology, Ministry of Education, Chongqing 400054, China;1. Zhejiang University of Technology, Zhengzhou, China;2. Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Brazil;3. Communications Group, Department of Electronic Engineering, University of York, United Kingdom |
| |
| Abstract: | In conventional PID-type iterative learning control (ILC) designs, to determine the learning control gains involved, relevant model knowledge on the controlled systems is often dependent. In this paper, two completely data-driven ILC laws, the extended PD-type ILC law and the extended P-type ILC law, are designed in frequency domain for linear discrete-time (LDT) single-input single-output (SISO) systems. The designs of the proposed ILC laws are based on the approximation/identification to unknown transfer function with a novel adaptive Fourier decomposition (AFD) technique. As a result, the strictly monotonic convergence of ILC tracking error is guaranteed in a deterministic way. A numerical example on a four-axis robot arm is performed to illustrate the effectiveness of the proposed data-driven ILC algorithms |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
