A unified data-driven design framework of optimality-based generalized iterative learning control |
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| Affiliation: | 1. School of Automation & Electronics Engineering, Qingdao University of Science & Technology, Qingdao 266042, PR China;2. Advanced Control Systems Lab, School of Electronics & Information Engineering, Beijing Jiaotong University, Beijing 100044, PR China;3. Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB, Canada T6G 2G6;1. Institute of Advanced Control Technology, Dalian University of Technology, Dalian 116024, PR China;2. Institute of Particle Science and Engineering, School of Process, Environmental and Materials Engineering, University of Leeds, Leeds LS2 9JT, UK;3. School of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou 510640, PR China;4. Department of Chemical Engineering, Chung-Yuan Christian University, Chung-Li, 32023, Taiwan;1. Division of Automatic Control, Department of Electrical Engineering, Linköping University, SE-581 83 Linköping, Sweden;2. Nira Dynamics, Teknikringen 6, SE-583 30 Linköping, Sweden;3. ABB AB–Robotics, SE-721 68 Västerås, Sweden;1. Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore;2. Department of Electrical and Computer Engineering, National University of Singapore, Singapore;3. Department of Aeronautics, Imperial College London, United Kingdom;4. Department of Electrical and Electronic Engineering, University of Melbourne, Australia |
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| Abstract: | This paper proposes a unified design framework for data-driven optimality-based generalized iterative learning control (DDOGILC), including data-driven optimal ILC (DDOILC), data-driven optimal point-to-point ILC (DDOPTPILC), and data-driven optimal terminal ILC (DDTILC). First, a dynamical linearization in the iteration domain is developed. Then three specific DDOGILC approaches are proposed. Both design and analysis of the controller only require the measured I/O data without relying on any explicit model information. The optimal learning gain can be updated iteratively, which makes the proposed DDOGILC more adaptable to the changes in the plant. Furthermore, the proposed DDOPTPILC and DDOTILC only depend on the tracking error at specific points, and thus they can deal with the scenario when the system outputs are measured only at some time instants. Moreover, the proposed DDOPTPILC and DDOTILC approaches do not need to track the unnecessary output reference points so that the convergence performance is improved. |
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| Keywords: | Data-driven control Norm optimal design Iterative learning control Point-to-point iterative learning control Terminal iterative learning control Nonlinear discrete-time systems |
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