Complex-Track Following in Real-Time Using Model-Based Predictive Control

2020 ◽  
Author(s):  
Wael Farag
Keyword(s):  
Real Time ◽  
Predictive Control ◽  
Model Based

Nonlinear Model Predictive Control Experiments on a Laboratory Gas Turbine Installation

2000 ◽  
Author(s):  
H. A. van Essen ◽  
H. C. de Lange
Keyword(s):  
Model Predictive Control ◽  
Real Time ◽  
Gas Turbine ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Computation Time ◽  
Nonlinear Model Predictive Control ◽  
Control Performance ◽  
Model Based ◽  
Turbine Installation

Results on the feasibility and benefits of model based predictive control applied to a gas turbine are presented. For a laboratory gas turbine installation, the required dynamic simulation model and the real-time (nonlinear) Model Predictive Control (MPC) implementation are discussed. Results on both model validation and control performance are presented. We applied a nonlinear MPC configuration to control the laboratory gas turbine installation and succeeded in a real-time implementation. Although the available computation time for prediction and optimization of the model limits the sample time, the advantages of MPC, i.e. constraint handling, and anticipation to future (set-point) changes are fully reached, and the control performance is good. Special attention is paid to the performance of the applied filter that compensates for inevitable mismatches between model and process measurements. In general, the opportunities of model based control of turbomachinery are promising.

scholarly journals Modified Volterra model-based non-linear model predictive control of IC engines with real-time simulations

2016 ◽  
Vol 39 (2) ◽  
pp. 208-223 ◽  
Author(s):  
Yiran Shi ◽  
Ding-Li Yu ◽  
Yantao Tian ◽  
Yaowu Shi
Keyword(s):  
Model Predictive Control ◽  
Real Time ◽  
Linear Model ◽  
Predictive Control ◽  
Fuel Injection ◽  
Volterra Model ◽  
Ic Engine ◽  
Model Based ◽  
Significant Difference ◽  
Non Linear

Modelling of non-linear dynamics of an air manifold and fuel injection in an internal combustion (IC) engine is investigated in this paper using the Volterra series model. Volterra model-based non-linear model predictive control (NMPC) is then developed to regulate the air–fuel ratio (AFR) at the stoichiometric value. Due to the significant difference between the time constants of the air manifold dynamics and fuel injection dynamics, the traditional Volterra model is unable to achieve a proper compromise between model accuracy and complexity. A novel method is therefore developed in this paper by using different sampling periods, to reduce the input terms significantly while maintaining the accuracy of the model. The developed NMPC system is applied to a widely used IC engine benchmark, the mean value engine model. The performance of the controlled engine under real-time simulation in the environment of dSPACE was evaluated. The simulation results show a significant improvement of the controlled performance compared with a feed-forward plus PI feedback control.

Nonlinear Model Predictive Control Experiments on a Laboratory Gas Turbine Installation

2001 ◽  
Vol 123 (2) ◽  
pp. 347-352 ◽  
Author(s):  
H. A. van Essen ◽  
H. C. de Lange
Keyword(s):  
Model Predictive Control ◽  
Real Time ◽  
Gas Turbine ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Computation Time ◽  
Nonlinear Model Predictive Control ◽  
Control Performance ◽  
Model Based ◽  
Turbine Installation

Results on the feasibility and benefits of model based predictive control applied to a gas turbine are presented. For a laboratory gas turbine installation, the required dynamic simulation model and the real-time (nonlinear) model predictive control (MPC) implementation are discussed. Results on both model validation and control performance are presented. We applied a nonlinear MPC configuration to control the laboratory gas turbine installation and succeeded in a real-time implementation. Although the available computation time for prediction and optimization of the model limits the sample time, the advantages of MPC, i.e. constraint handling, and anticipation to future (set-point) changes are fully reached, and the control performance is good. Special attention is paid to the performance of the applied filter that compensates for inevitable mismatches between model and process measurements. In general, the opportunities of model based control of turbomachinery are promising.

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