A design method for indirect iterative learning control based on two-dimensional generalized predictive control algorithm |
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| Affiliation: | 1. Key Lab for IOT and Information Fusion Technology of Zhejiang, Information and Control Institute, Hangzhou Dianzi University, Hangzhou 310018, PR China;2. Institute of Automation, Beijing University of Chemical Technology, Beijing 100029, PR China;3. Department of Chemical and Biomolecular Engineering, Hong Kong University of Science and Technology, Hong Kong;1. School of Automation & Electronic 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;1. Shanghai Key Laboratory of Power Station Automation Technology, Department of Automation, College of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200072, China;2. National University of Singapore, Department of Chemical and Biomolecular Engineering, Singapore 117576, Singapore;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 |
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| Abstract: | Indirect iterative learning control (ILC) facilitates the application of learning-type control strategies to the repetitive/batch/periodic processes with local feedback control already. Based on the two-dimensional generalized predictive control (2D-GPC) algorithm, a new design method is proposed in this paper for an indirect ILC system which consists of a model predictive control (MPC) in the inner loop and a simple ILC in the outer loop. The major advantage of the proposed design method is realizing an integrated optimization for the parameters of existing feedback controller and design of a simple iterative learning controller, and then ensuring the optimal control performance of the whole system in sense of 2D-GPC. From the analysis of the control law, it is found that the proposed indirect ILC law can be directly obtained from a standard GPC law and the stability and convergence of the closed-loop control system can be analyzed by a simple criterion. It is an applicable and effective solution for the application of ILC scheme to the industry processes, which can be seen clearly from the numerical simulations as well as the comparisons with the other solutions. |
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| Keywords: | Iterative learning control (ILC) Two-dimensional generalized predictive control (2D-GPC) Indirect ILC Model predictive control (MPC) Repetitive/batch/periodic processes |
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