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A COMPARATIVE STUDY ON COMPUTATIONAL SCHEMES FOR NONLINEAR MODEL PREDICTIVE CONTROL |
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| Authors: | Vu Trieu Minh Nitin Afzulpurkar |
| Affiliation: | 1. Industrial Systems Engineering Program School of Advanced Technologies, Asian Institute of Technology, Thailand
Vu Trieu Minh: graduated with B.E. of Mechanical in 1983 at Hanoi University of Technology, Master and Ph.D. of Mechatronics at Asian Institute of Technology (AIT) in 1999 and 2004 respectively under the supervision of Asso. Prof., Dr. Nitin Afzulpurkar. Awarded the PetroVietnam scholarship for the Master and the Austrian Government scholarship for the Ph.D. program at AIT. Awarded DAAD fellowship for conducting advanced control research at Augsburg University, Germany. Currently he is a research specialist at Mechatronics, AIT. His major research interests are Dynamical Systems, Model Based Control Algorithms for Advanced Process Control. vutrieuminh@yahoo.com;2. Industrial Systems Engineering Program School of Advanced Technologies, Asian Institute of Technology, Thailand Nitin Afzulpurkar: is currently an Associate Professor and the Coordinator of the Mechatronics and the Microelectronics Program, Asian Institute of Technology, Thailand. He obtained Ph.D. from University of Canterbury, New Zealand in Mechanical Engineering with specialization in Robotics. He has previously worked in India, New Zealand, Japan and Hong Kong. He has authored over fifty research papers in the field of Robotics, Mechatronics and MEMS. His current research interests are Computer Vision, Mechatronics systems, MEMS and Intelligent Automation. He is a member of IEEE. |
| Abstract: | This paper briefly reviews development of nonlinear model predictive control (NMPC) schemes for finite horizon prediction and basic computational algorithms that can solve the stable real‐time implementation of NMPC in space state form with state and input constraints. In order to ensure stability within a finite prediction horizon, most NMPC schemes use a terminal region constraint at the end of the prediction horizon — a particular NMPC scheme using a terminal region constraint, namely quasi‐infinite horizon, that guarantees asymptotic closed‐loop stability with input constraints is presented. However, when nonlinear processes have both input and state constraints, difficulty arises from failure to satisfy the state constraints due to constraints on input. Therefore, a new NMPC scheme without a terminal region constraint is developed using soften state constraints. A brief comparative simulation study of two NMPC schemes: quasi‐infinite horizon and soften state constraints is done via simple nonlinear examples to demonstrate the ability of the soften state constraints scheme. Finally, some features of future research from this study are discussed. |
| Keywords: | Model predictive control nonlinear model predictive control terminal region quasi‐infinite horizon soften state constraints |
