IMPROVED ROBUSTNESS OF MULTIVARIABLE MODEL PREDICTIVE CONTROL UNDER MODEL UNCERTAINTIES

2007 ◽  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Multivariable Model ◽  
Model Uncertainties

Multirate multivariable model predictive control and its application to a polymerization reactor

1994 ◽  
Vol 59 (3) ◽  
pp. 731-742 ◽  
Author(s):  
MASAHIRO OHSHIMA ◽  
IORI HASHIMOTO ◽  
HIROMU OHNO ◽  
MAKOTO TAKEDA ◽  
TAKASHI YONEYAMA ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Multivariable Model ◽  
Polymerization Reactor

scholarly journals Effect of Control Horizon in Model Predictive Control for Steam/Water Loop in Large-Scale Ships

Processes ◽  
2018 ◽  
Vol 6 (12) ◽  
pp. 265 ◽  
Author(s):  
Shiquan Zhao ◽  
Anca Maxim ◽  
Sheng Liu ◽  
Robin De Keyser ◽  
Clara Ionescu
Keyword(s):  
Computational Complexity ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Large Scale ◽  
Multivariable Model ◽  
Framework Analysis ◽  
Optimization Task ◽  
Control Framework ◽  
Good Trade ◽  
The Cost

This paper presents an extensive analysis of the properties of different control horizon sets in an Extended Prediction Self-Adaptive Control (EPSAC) model predictive control framework. Analysis is performed on the linear multivariable model of the steam/water loop in large-scale watercraft/ships. The results indicate that larger control horizon values lead to better loop performance, at the cost of computational complexity. Hence, it is necessary to find a good trade-off between the performance of the system and allocated or available computational complexity. In this original work, this problem is explicitly treated as an optimization task, leading to the optimal control horizon sets for the steam/water loop example. Based on simulation results, it is concluded that specific tuning of control horizons outperforms the case when only a single valued control horizon is used for all the loops.

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