Design of Model Predictive Controller for a Four-Tank Process Using Linear State Space Model and Performance Study for Reference Tracking under Disturbances

Author(s):  
S. A. Nirmala ◽  
B. Veena Abirami ◽  
D. Manamalli
2012 ◽  
Vol 246-247 ◽  
pp. 311-316
Author(s):  
Xiao Suo Luo

In order to deal with nonlinear, time-varying and disturbance-involved characteristics in the practical industrial processes, an indirect adaptive state-space MPC (model predictive control) method based on subspace identification is proposed. The state-space model, obtained through the POMOESP (Past Output MOESP, MOESP is one form of the subspace identification methods) algorithm, is regarded as the system model. Then, this model is used to design the model predictive controller that involves the solution of a quadratic programming problem to constraints. This controller is applied to the process control simulation on a 2-CSTR. Through comparisons of performance with a linear state-space MPC scheme, the superiority of the proposed control method is illustrated.


2014 ◽  
Vol 26 (1) ◽  
pp. 29-38 ◽  
Author(s):  
J. Bessac ◽  
P. Ailliot ◽  
V. Monbet

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Mapopa Chipofya ◽  
Deok Jin Lee ◽  
Kil To Chong

This paper presents a solution to stability and trajectory tracking of a quadrotor system using a model predictive controller designed using a type of orthonormal functions called Laguerre functions. A linear model of the quadrotor is derived and used. To check the performance of the controller we compare it with a linear quadratic regulator and a more traditional linear state space MPC. Simulations for trajectory tracking and stability are performed in MATLAB and results provided in this paper.


animal ◽  
2014 ◽  
Vol 8 (3) ◽  
pp. 477-483
Author(s):  
J. Detilleux ◽  
L. Theron ◽  
E. Reding ◽  
C. Bertozzi ◽  
C. Hanzen

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