Linear matrix inequality-based robust model predictive control for time-delayed systems

2012 ◽  
Vol 6 (1) ◽  
pp. 37 ◽  
Author(s):  
B.D.O. Capron ◽  
M.T. Uchiyama ◽  
D. Odloak
Keyword(s):  
Model Predictive Control ◽  
Linear Matrix Inequality ◽  
Predictive Control ◽  
Linear Matrix ◽  
Matrix Inequality ◽  
Delayed Systems ◽  
Robust Model Predictive Control ◽  
Robust Model

scholarly journals Robust Model Predictive Control of Networked Control Systems under Input Constraints and Packet Dropouts

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Deyin Yao ◽  
Hamid Reza Karimi ◽  
Yiyong Sun ◽  
Qing Lu
Keyword(s):  
Model Predictive Control ◽  
Linear Matrix Inequality ◽  
Predictive Control ◽  
Infinite Horizon ◽  
State Observer ◽  
Observer Design ◽  
Linear Matrix ◽  
Matrix Inequality ◽  
Robust Model Predictive Control ◽  
Robust Model

This paper deals with the problem of robust model predictive control (RMPC) for a class of linear time-varying systems with constraints and data losses. We take the polytopic uncertainties into account to describe the uncertain systems. First, we design a robust state observer by using the linear matrix inequality (LMI) constraints so that the original system state can be tracked. Second, the MPC gain is calculated by minimizing the upper bound of infinite horizon robust performance objective in terms of linear matrix inequality conditions. The method of robust MPC and state observer design is illustrated by a numerical example.

scholarly journals Robust Model Predictive Control Schemes for Tracking Setpoints

2010 ◽  
Vol 2010 ◽  
pp. 1-9 ◽  
Author(s):  
Vu Trieu Minh ◽  
Fakhruldin Bin Mohd Hashim
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Uncertain Systems ◽  
State Constraints ◽  
Weighting Factor ◽  
The State ◽  
Future Research ◽  
Linear Matrix ◽  
Robust Model Predictive Control ◽  
Robust Model

This paper briefly reviews the development of nontracking robust model predictive control (RMPC) schemes for uncertain systems using linear matrix inequalities (LMIs) subject to input saturated and softened state constraints. Then we develop two new tracking setpoint RMPC schemes with common Lyapunov function and with zero terminal equality subject to input saturated and softened state constraints. The novel tracking setpoint RMPC schemes are able to stabilize uncertain systems once the output setpoints lead to the violation of the state constraints. The state violation can be regulated by changing the value of the weighting factor. A brief comparative simulation study of the two tracking setpoint RMPC schemes is done via simple examples to demonstrate the ability of the softened state constraint schemes. Finally, some features of future research from this study are discussed.

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