scholarly journals Generalized terminal state constraint for model predictive control

Automatica ◽  
2013 ◽  
Vol 49 (9) ◽  
pp. 2622-2631 ◽  
Author(s):  
Lorenzo Fagiano ◽  
Andrew R. Teel
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
State Constraint ◽  
Terminal State ◽  
Terminal State Constraint

Robust Control with a Terminal State Constraint

1995 ◽  
Vol 28 (8) ◽  
pp. 373-376
Author(s):  
A.V. Savkin ◽  
I.R. Petersen
Keyword(s):  
Robust Control ◽  
State Constraint ◽  
Terminal State ◽  
Terminal State Constraint

Robust control with a terminal state constraint

Automatica ◽  
1996 ◽  
Vol 32 (7) ◽  
pp. 1001-1005 ◽  
Author(s):  
Andrey V. Savkin ◽  
Ian R. Petersen
Keyword(s):  
Robust Control ◽  
State Constraint ◽  
Terminal State ◽  
Terminal State Constraint

scholarly journals Getting a Suitable Terminal Cost and Maximizing the Terminal Region for MPC

2010 ◽  
Vol 2010 ◽  
pp. 1-16 ◽  
Author(s):  
Wang Ya-feng ◽  
Sun Fu-chun ◽  
Zhang You-an ◽  
Liu Hua-ping ◽  
Min Haibo
Keyword(s):  
Support Vector Machine ◽  
Model Predictive Control ◽  
Predictive Control ◽  
State Constraint ◽  
Terminal Region ◽  
Support Vector ◽  
Simple Method ◽  
Iterative Estimation ◽  
Total Cost

The model predictive control (MPC) subject to control and state constraint is studied. Given a terminal cost, a terminal region is obtained through iterative estimation by using support vector machine (SVM). It is proved that the obtained terminal region is the largest terminal region when the terminal cost is given. The relationships between terminal cost and terminal region and between terminal cost and total cost are discussed, respectively. Based on these relationships, a simple method to get a suitable terminal cost is proposed and it can be adjusted according to our need. Finally, some experiment results are presented.

scholarly journals Desired Terminal State Concept in Model Predictive Control: A Case Study

2019 ◽  
Vol 2019 ◽  
pp. 1-7
Author(s):  
František Dušek ◽  
Daniel Honc
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Method ◽  
Optimization Methods ◽  
Terminal State ◽  
Online Optimization ◽  
Transient Behaviour ◽  
The Stability ◽  
Transport Problems ◽  
And Control

The paper deals with an online optimization control method for dynamical processes called Model Predictive Control (MPC). It is a popular control method in industry and frequently treated in academic areas as well. The standard predictive controllers usually do not guarantee stability especially for the case of short horizons and large control error penalization. Terminal state is one way to ensure stability or at least increase the controller robustness. In the paper, deviation of the predicted terminal state from the desired terminal state is considered as one term of the cost function. Effect of the stability and control quality is demonstrated in the simulated experiments. The application area for online optimization methods is very broad including various logistics and transport problems. If the dynamics of the controlled processes cannot be neglected, the optimization problem must be solved not only for steady state but also for transient behaviour, e.g., by MPC.

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