Nonlinear Model Predictive Control of a Laboratory Gas Turbine Installation

Dynamic simulation is a great design tool in selecting control strategies and studying of the dynamic response of the engine. A model based on physical description of the plant components has been derived for control purposes, it is complex and highly non-linear but able to provide a more detailed description of the power plant. The nonlinear dynamic model is developed in order to simulate the dynamic behavior of the gas turbine, to provide a fast and reliable model for the syntheses of the controller. The complete model comprise of seven differential equations obtained by applying mass and energy balance to each of three components of the plant, including one equation for mechanical energy for the rotor shaft. The non-linear model is linearised in order to design a Single-Input, Single-Output (SISO) controller for the turbine, and an adaptive self-tuning pole assignment controller is considered and designed for the turbine. The models are simulated using the MATLAB and SIMULINK software.The results of the dynamic simulation appear reasonable and confirm that the plant and the selected control will result a safe and efficient operation of the plant under steady state and transient conditions. The output of the theoretical model under a step input was compared to experimentally collected data from the gas turbine. The results of the comparison show that the theoretically derived model can satisfactorily represent the system.

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“…[12,13]. In this study the controller used is the indirect pole assignment selftuning controller developed by Wellstead et.…”

Section: Self−tuning Controllermentioning

confidence: 99%

Dynamic Modeling and Self-Tuning Control of a Gas Turbine

Zawia

Al-Khodari

2004

Volume 2: Turbo Expo 2004

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“…The application of MPC to control the gas turbine was introduced by van Essen (1998) and Vroemen et al (1999). Model-based control schemes are highly related to the accuracy of the process model.…”

Section: Introductionmentioning

confidence: 99%

Hammerstein-model-based predictive control of micro-turbines

Jurado

2006

Int. J. Energy Res.

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SUMMARYMicro-turbine operation is restricted due to mechanical, thermal, and flow limitations. Model predictive control is selected because it can explicitly handle the nonlinearities, and constraints of many variables in a single control formulation. The Hammerstein models are particular kinds of nonlinear systems where the nonlinear block is static and is accompanied by a linear system. This paper suggests a model-based controller for the regulation of a micro-turbine. The performances using both the linear and Hammerstein models are studied with constraints. The results show the capabilities of the proposed control to track a periodic reference trajectory and a significantly better closed-loop response.

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“…The system to be investigated is a gas turbine. Various approaches such as Fuzzy [8], MPC [9], and Neural Network [10] are used for control of different types of this system. The aim of the present paper is to design an optimal LQG/LTR controller for this system.…”

Section: Introductionmentioning

confidence: 99%

Using LQG/LTR Optimal Control Method to Improve Stability and Performance of Industrial Gas Turbine System

Shabaninia

Jafari

2012

ISRN Electronics

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The gas turbine is a power plant, which produces a great amount of energy for its size and weight. Its compactness, low weigh, and multiple fuels make it a natural power plant for various industries such as power generation or oil and gas process plants. In any of these applications, the performance and stability of the gas turbines are the end products that strongly influence the profitability of the business that employs them. Control and analyses of gas turbines for achieving stability and good performance are important so that they have to operate for prolong period. Effective control system design usually benefits from an accurate dynamic model of the plant. Characteristic component parts of the system are considered in this model. Gas turbine system is described by specified thermodynamic equations that can be used for defining its model. This paper introduces an optimal LQG/LTR control method for a gas turbine. Analysing the gas turbine dynamic in time and frequency domain by using proposed control compared to PID controller is followed. Applying this optimal control method can provide good performance and stability for the component parts of system.

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Nonlinear Model Predictive Control of a Laboratory Gas Turbine Installation

Cited by 33 publications

References 7 publications

Dynamic Modeling and Self-Tuning Control of a Gas Turbine

Dynamic Modeling and Self-Tuning Control of a Gas Turbine

Hammerstein-model-based predictive control of micro-turbines

Using LQG/LTR Optimal Control Method to Improve Stability and Performance of Industrial Gas Turbine System

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