Nonlinear Model Predictive Control for Thrust Tracking of a Gas Turbine

2006 ◽  
Author(s):  
Vivek Diwanji ◽  
Amit Godbole ◽  
Nitesh Waghode
Keyword(s):  
Model Predictive Control ◽  
Gas Turbine ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Nonlinear Model Predictive Control

Nonlinear Model Predictive Control for Gas Turbine Engines

2004 ◽  
Author(s):  
Junxia Mu ◽  
David Rees
Keyword(s):  
Model Predictive Control ◽  
Gas Turbine ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Process Model ◽  
Nonlinear Model Predictive Control ◽  
Control Performance ◽  
Turbine Engine ◽  
Hard Constraints ◽  
Wide Range

In this paper Nonlinear Model Predictive Control (NMPC) is applied to a gas turbine engine. Since the performance of model based control schemes is highly dependent on the accuracy of the process model, the estimation of global nonlinear gas turbine models using NARMAX and neural network is first examined. To solve the NMPC problem, the Newton-based Levenberg-Marquardt Approach (NLMA) with hard constraints and Sequential Quadratic Programming (SQP) with soft constraints are validated using a wide range of large random, small and ramp signal tests. It is shown that the control performance using SQP is slightly better than that of NLMA, and proposed methods are robust in the face of large disturbances and model uncertainties. The results presented illustrate the improvement in the control performance using both methods over against gain-scheduling PID controllers.

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.

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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