An Approach to linear state Signal Shaping by quadratic Model Predictive Control

2018 ◽  
Author(s):  
Carlos Cateriano Yanez ◽  
Georg Pangalos ◽  
Gerwald Lichtenberg
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Quadratic Model ◽  
Linear State

scholarly journals Quadratic Model Predictive Control Including Input Cardinality Constraints

2017 ◽  
Vol 62 (6) ◽  
pp. 3068-3075 ◽  
Author(s):  
Ricardo P. Aguilera ◽  
Gabriel Urrutia ◽  
Ramon A. Delgado ◽  
Daniel Dolz ◽  
Juan C. Aguero
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Quadratic Model ◽  
Cardinality Constraints

scholarly journals Robust Output Model Predictive Control of an Unstable Rijke Tube

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Fabian Jarmolowitz ◽  
Christopher Groß-Weege ◽  
Thomas Lammersen ◽  
Dirk Abel
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
State Feedback ◽  
Linear Matrix ◽  
Matrix Inequalities ◽  
Rijke Tube ◽  
Guaranteed Stability ◽  
Predictive Controller ◽  
Linear State ◽  
Gain Scheduled

This work investigates the active control of an unstable Rijke tube using robust output model predictive control (RMPC). As internal model a polytopic linear system with constraints is assumed to account for uncertainties. For guaranteed stability, a linear state feedback controller is designed using linear matrix inequalities and used within a feedback formulation of the model predictive controller. For state estimation a robust gain-scheduled observer is developed. It is shown that the proposed RMPC ensures robust stability under constraints over the considered operating range.

scholarly journals Constrained Linear State Signal Shaping Model Predictive Control for Harmonic Compensation in Power Systems

2020 ◽  
Vol 53 (2) ◽  
pp. 6937-6942
Author(s):  
Kathrin Weihe ◽  
Carlos Cateriano Yáñez ◽  
Georg Pangalos ◽  
Gerwald Lichtenberg
Keyword(s):  
Model Predictive Control ◽  
Power Systems ◽  
Predictive Control ◽  
Harmonic Compensation ◽  
Linear State

Model Predictive Control of Circulating Fluidized Bed Coal Combustor

2013 ◽  
Vol 846-847 ◽  
pp. 73-76 ◽  
Author(s):  
Shi Li ◽  
Yan Hu ◽  
Xi Ju Zong
Keyword(s):  
Model Predictive Control ◽  
Fluidized Bed ◽  
Predictive Control ◽  
Circulating Fluidized Bed ◽  
Model Simulation ◽  
State Space Model ◽  
Space Model ◽  
Nonlinear Mechanical ◽  
State Point ◽  
Linear State

This paper is concerns with the study of model predictive control of a circulating fluidized bed coal combustor. A nonlinear mechanical model is proposed based on a pilot plant and representative of the main combustion phenomena. The nonlinear model is linearized at the steady-state point, linear state-space model is obtained. Model predictive control (MPC) strategy is applied using linear model. Simulation results are presented and discussed.

scholarly journals Offset-free nonlinear Model Predictive Control with state-space process models

2017 ◽  
Vol 27 (4) ◽  
pp. 595-615 ◽  
Author(s):  
Piotr Tatjewski
Keyword(s):  
Model Predictive Control ◽  
State Estimation ◽  
State Space ◽  
Predictive Control ◽  
Conventional Approach ◽  
Process Models ◽  
External Disturbances ◽  
Disturbance Estimation ◽  
Constant State ◽  
Linear State

AbstractOffset-free model predictive control (MPC) algorithms for nonlinear state-space process models, with modeling errors and under asymptotically constant external disturbances, is the subject of the paper. The main result of the paper is the presentation of a novel technique based on constant state disturbance prediction. It was introduced originally by the author for linear state-space models and is generalized to the nonlinear case in the paper. First the case with measured state is considered, in this case the technique allows to avoid disturbance estimation at all. For the cases with process outputs measured only and thus the necessity of state estimation, the technique allows the process state estimation only - as opposed to conventional approach of extended process-and-disturbance state estimation. This leads to simpler design with state observer/filter of lower order and, moreover, without the need of a decision of disturbance placement in the model (under certain restrictions), as in the conventional approach. A theoretical analysis of the proposed algorithm is provided, under applicability conditions which are weaker than in the conventional approach. The presented theory is illustrated by simulation results of nonlinear processes, showing competitiveness of the proposed algorithms.

Sign in / Sign up

Export Citation Format

Share Document