Fast model predictive control based on linear input/output models and bounded-variable least squares

2017 ◽  
Author(s):  
Nilay Saraf ◽  
Alberto Bemporad
Keyword(s):  
Model Predictive Control ◽  
Least Squares ◽  
Predictive Control ◽  
Input Output

scholarly journals Convergence Guaranteed Nonlinear Constraint Model Predictive Control via I/O Linearization

2013 ◽  
Vol 2013 ◽  
pp. 1-9
Author(s):  
Xiaobing Kong ◽  
Xiangjie Liu ◽  
Xiuming Yao
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Output Feedback ◽  
Feedback Linearization ◽  
Optimal Solution ◽  
Quadratic Program ◽  
Control Input ◽  
Input Output ◽  
Nonlinear Constraint ◽  
Constraint Model

Constituting reliable optimal solution is a key issue for the nonlinear constrained model predictive control. Input-output feedback linearization is a popular method in nonlinear control. By using an input-output feedback linearizing controller, the original linear input constraints will change to nonlinear constraints and sometimes the constraints are state dependent. This paper presents an iterative quadratic program (IQP) routine on the continuous-time system. To guarantee its convergence, another iterative approach is incorporated. The proposed algorithm can reach a feasible solution over the entire prediction horizon. Simulation results on both a numerical example and the continuous stirred tank reactors (CSTR) demonstrate the effectiveness of the proposed method.

scholarly journals Identification and Predictive Control by p-norm Minimization

10.14311/802 ◽  
2006 ◽  
Vol 46 (1) ◽  
Author(s):  
J. Pekař ◽  
J. Štecha
Keyword(s):  
Least Squares ◽  
Predictive Control ◽  
System Parameter ◽  
Total Least Squares ◽  
Input Output ◽  
Time System ◽  
Output Data ◽  
Real Time System ◽  
Norm Minimization ◽  
System Equations

Real time system parameter estimation from the set of input-output data is usually solved by minimization of quadratic norm errors of system equations – known in the literature as least squares (LS) or its modification as total least squares (TLS) or mixed LS and TLS. It is known that the utilization of the p-norm (1?p

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