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.

scholarly journals LVD-NMPC: A learning-based vision dynamics approach to nonlinear model predictive control for autonomous vehicles

2021 ◽  
Vol 18 (3) ◽  
pp. 172988142110195
Author(s):  
Sorin Grigorescu ◽  
Cosmin Ginerica ◽  
Mihai Zaha ◽  
Gigel Macesanu ◽  
Bogdan Trasnea
Keyword(s):  
Neural Network ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Autonomous Vehicles ◽  
Process Model ◽  
Network Performance ◽  
Vision System ◽  
Nonlinear Model Predictive Control ◽  
Scaled Model

In this article, we introduce a learning-based vision dynamics approach to nonlinear model predictive control (NMPC) for autonomous vehicles, coined learning-based vision dynamics (LVD) NMPC. LVD-NMPC uses an a-priori process model and a learned vision dynamics model used to calculate the dynamics of the driving scene, the controlled system’s desired state trajectory, and the weighting gains of the quadratic cost function optimized by a constrained predictive controller. The vision system is defined as a deep neural network designed to estimate the dynamics of the image scene. The input is based on historic sequences of sensory observations and vehicle states, integrated by an augmented memory component. Deep Q-learning is used to train the deep network, which once trained can also be used to calculate the desired trajectory of the vehicle. We evaluate LVD-NMPC against a baseline dynamic window approach (DWA) path planning executed using standard NMPC and against the PilotNet neural network. Performance is measured in our simulation environment GridSim, on a real-world 1:8 scaled model car as well as on a real size autonomous test vehicle and the nuScenes computer vision dataset.

Robust Nonlinear Model Predictive Control for Infrared Drying of Automotive Coatings

2015 ◽  
Author(s):  
Xiaoqing Cao ◽  
Beshah Ayalew
Keyword(s):  
Moisture Content ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Process Model ◽  
Unscented Kalman Filter ◽  
Nonlinear Model Predictive Control ◽  
Average Moisture Content ◽  
Coating Film ◽  
Modeling Uncertainties

In this paper, a scheme for estimation and control of moisture content in infrared (IR) drying of paints/coatings is proposed. To deal with the infinite-dimensional nature of the process model associated with the moisture diffusion in the coating film, POD-Galerkin method is first applied for model reduction. Then, an unscented Kalman filter (UKF) is devised for distributed moisture content estimation and nonlinear model predictive control (NMPC) system is designed for tracking a desired average moisture content profile with optimized energy needs. To enhance the control performance in the presence of potential modeling uncertainties, a robust design is also included in the proposed NMPC scheme. The effectiveness of this approach is demonstrated via simulated applications to IR drying of automotive waterborne coatings.

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