Model Predictive Control and Controller Parameter Optimisation of Combustion Instabilities

2019 ◽  
Vol 36 (2) ◽  
pp. 185-194 ◽  
Author(s):  
I. Yazar ◽  
F. Caliskan ◽  
R. Vepa
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Closed Loop ◽  
Combustion Process ◽  
Open Loop ◽  
Loop Model ◽  
Parameter Optimisation ◽  
Direct Adaptive Control ◽  
The Cost ◽  
Adaptive Control Algorithm

Abstract In this paper the application of model predictive control (MPC) to a two-mode model of the dynamics of the combustion process is considered. It is shown that the MPC by itself does not stabilize the combustor and the control gains obtained by applying the MPC algorithms need to be optimized further to ensure that the phase difference between the two modes is also stable. The results of applying the algorithm are compared with the open loop model amplitude responses and to the closed loop responses obtained by the application of a direct adaptive control algorithm. It is shown that the MPC coupled with the cost parameter optimisation proposed in the paper, always guarantees the closed loop stability, a feature that may not always be possible with an adaptive implementations.

scholarly journals A Heuristic Parameter-Dependent Open-Loop Model Predictive Control

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 135364-135370
Author(s):  
Xin Zan ◽  
Lu Jin ◽  
Jun Wang
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Open Loop ◽  
Loop Model ◽  
Parameter Dependent

Approximating open-loop and closed-loop optimal control by model predictive control

2020 ◽  
Author(s):  
Asen L. Dontchev ◽  
Ilya V. Kolmanovsky ◽  
Mikhail I. Krastanov ◽  
Vladimir M. Veliova
Keyword(s):  
Optimal Control ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Closed Loop ◽  
Open Loop

Model Predictive Control for Gas Turbine Engines

2018 ◽  
Author(s):  
Amit Pandey ◽  
Maurício de Oliveira ◽  
Robert H. Moroto
Keyword(s):  
Model Predictive Control ◽  
Gas Turbine ◽  
Predictive Control ◽  
Closed Loop ◽  
Open Loop ◽  
Turbine Engines ◽  
Gas Turbine Engines ◽  
Physical Constraints ◽  
Classical Control ◽  
Predictive Controllers

The use of Model Predictive Control (MPC) is commonplace in many industrial applications. The anticipative nature of MPC and the inclusion of physical constraints into the control framework presents many advantages over classical control strategies. Despite these advantages, obtaining an accurate open-loop model of the underlying process is often a difficult and time consuming process. In this paper, a methodology is introduced to identify linear open-loop models of gas turbine engines from closed-loop data. The closed-loop data can be obtained by any sufficiently informative experiment from a plant in operation or simulation. We present simulation results here. These open-loop models are then used in the design of model predictive controllers at a number of operating points of the turbine. The predictive controllers we designed include physical constraints on the fuel and air flow into the turbine. The performance of these predictive controllers is compared in simulation against existing classical control techniques in a number of typical operating scenarios including off loads, on loads and set point changes.

scholarly journals Closed-loop model predictive control with mixed-integer optimization of a river reach with weirs

2019 ◽  
Vol 52 (23) ◽  
pp. 81-87
Author(s):  
Klaudia Horváth ◽  
Bart van Esch ◽  
Ivo Pothof ◽  
Tjerk Vreeken ◽  
Jan Talsma ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Closed Loop ◽  
Mixed Integer ◽  
Loop Model ◽  
Integer Optimization ◽  
Mixed Integer Optimization ◽  
River Reach
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