Iterative learning model predictive control for constrained multivariable control of batch processes |
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| Affiliation: | 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. School of Automation & Electronics Engineering, Qingdao University of Science & Technology, Qingdao 266042, PR China;2. School of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266042, PR China;3. Advanced Control Systems Lab, School of Electronics & Information Engineering, Beijing Jiaotong University, Beijing 100044, PR China;4. Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB T6G 2G6, Canada;1. Institute of Cyber-Systems and Control, National Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, 310027 Zhejiang, China;2. Department of Chemical Engineering, Chung-Yuan Christian University, Chung-Li 320, Taiwan, ROC |
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| Abstract: | In this paper, we propose a model predictive control (MPC) technique combined with iterative learning control (ILC), called the iterative learning model predictive control (ILMPC), for constrained multivariable control of batch processes. Although the general ILC makes the outputs converge to reference trajectories under model uncertainty, it uses open-loop control within a batch; thus, it cannot reject real-time disturbances. The MPC algorithm shows identical performance for all batches, and it highly depends on model quality because it does not use previous batch information. We integrate the advantages of the two algorithms. The proposed ILMPC formulation is based on general MPC and incorporates an iterative learning function into MPC. Thus, it is easy to handle various issues for which the general MPC is suitable, such as constraints, time-varying systems, disturbances, and stochastic characteristics. Simulation examples are provided to show the effectiveness of the proposed ILMPC. |
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| Keywords: | Iterative learning control Model predictive control Disturbance rejection Offset-free control Constrained multivariable control Iterative learning model predictive control |
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