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Model Predictive Control (MPC) is a class of computer control algorithms that solves an on-line optimization problem at each sampling point. Over the past 10-20 years, the popularity of MPC for industrial process control applications continued to increase as the effectiveness of various MPC techniques has been demonstrated in industrial applications. MPC has a set of tuning parameters, which can be used to fine-tune the closed-loop response for good performance and stability. Basically, these parameters are adjusted via a trial and error procedure, which is a cumbersome task due to their overlapping effect and due to non-linearity brought by input constraints
Some of my research work focused on developing online tuning methods for MPC using gradient methods or fuzzy logic. The diagram below shows the concept of online tuning procedure where a time-domain specification should be designed and integrated into the tuning algorithm. The algorithm will determines the favorable values of the controller tuning parameters that satisfy the desired specifications. |
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The following variation of the tunning algorithm is available for free download:
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For further reading consult the following publications:
Emad Ali, ``On-line tuning Strategy for PI Control Algorithms``, Journal of King Saud University, 11, Eng. Sci. (1), 49-70, 1999
Ashraf Ghazzawai, Emad Ali, Adnan Noah, and E. Zafiriou, “On-line Tuning Strategy for Model Predictive Controllers”, Journal Process Control, 11(3), 265-284, 2001.
Emad Ali, “pH Control Using PI Control Algorithms with Automatic Tuning Method ", IchemE, 79(5A), 611-620, 2001.
Emad Ali, “Automatic Tuning of Model Predictive Controllers Based on Fuzzy Logic”, Proceeding of IASTED Conference on Control and Applications, Banff, Canada, June 27-29, 136-142, 2001.
Emad Ali, ``Online Tuning Strategy for Multi-loop SISO PI Control Algorithms in Multivariable Interactive Systems``, Journal of King Saud University, 14, Eng. Sci. (2), 183-198, 2002.
Emad Ali “On-line Tuning Strategy for PI Control Algorithms Based on Simple Linear Models”, J. Chemical Engineering of Japan, 35(4), 324-333, 2002.
Emad Ali “Heuristic Online Tuning for Nonlinear Model Predictive Controestic llers using Fuzzy Logic”, J Process Control, 13(5), 383-396, 2003.
Emad Ali and Ashraf Ghazzawi, ”Online Tuning of Model Predictive Controllers Using Fuzzy Logic”, Canadian Journal of Chemical Engineering, 81, 1041-1051, 2003.
Emad Ali "Automatic Fuzzy Tuning of Proportional-Integral Controllers Based on Time-domain Specification", Journal of King Saud University, 17, Eng. Sci. (2), 171-196, 2005.
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