ILC strategy for progress improvement of economic performance in industrial model predictive control systems期刊界 All Journals 搜尽天下杂志传播学术成果专业期刊搜索期刊信息化学术搜索

ILC strategy for progress improvement of economic performance in industrial model predictive control systems

Affiliation:	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;1. ISCPIF, LIP6, France;2. LIP6, IRSTEA, France;3. IRSTEA, France;4. ISCPIF, France;5. INRA, UMR 782 GMPA génie Microbiologique des Procédés alimentaires, France;1. The State Key Lab of Fluid Power Transmission and Control, Zhejiang Province Key Laboratory of Advanced Manufacturing Technology, Zhejiang University, Hangzhou 310027, China;2. Kunshan Industrial Technology Research Institute Co., Ltd, Kunshan 215347, China;3. Department of Control Science & Engineering, College of Electronics & Information Engineering, Tongji University, Shanghai 201804, China;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. EXQUISITUS, Centre for E-City, School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore;1. Saab Aeronautics, SE-581 88 Linköping, Sweden;2. Division of Automatic Control, Department of Electrical Engineering, Linköping University, SE-581 83 Linköping, Sweden;3. Department of Automatic Control, Lund University, Box 118, SE-221 00 Lund, Sweden;4. ABB Robotics, SE-721 68 Västerås, Sweden

Abstract:	A novel approach to progress improvement of the economic performance in model predictive control (MPC) systems is developed. The conventional LQG based economic performance design provides an estimation which cannot be done by the controller while the proposed approach can develop the design performance achievable by the controller. Its optimal performance is achieved by solving economic performance design (EPD) problem and optimizing the MPC performance iteratively in contrast to the original EPD which has nonlinear LQG curve relationship. Based on the current operating data from MPC, EPD is transformed into a linear programming problem. With the iterative learning control (ILC) strategy, EPD is solved at each trial to update the tuning parameter and the designed condition; then MPC is conducted in the condition guided by EPD. The ILC strategy is proposed to adjust the tuning parameter based on the sensitivity analysis. The convergence of EPD by the proposed ILC has also been proved. The strategy can be applied to industry processes to keep enhancing the performance and to obtain the achievable optimal EPD. The performance of the proposed method is illustrated via an SISO numerical system as well as an MIMO industry process.

Keywords:	Economic performance assessment Iterative learning control LQG Model predictive control Sensitivity analysis
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