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

Shabaninia, Faridoon; Jafari, Kazem

doi:10.5402/2012/134580

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…The most systematic and popular method to find K is to minimize the quadratic performance index J=∫ x^T Qx+u^T Ru)dt (6) Where Q and R are the positive-definite Hermitian or real symmetric matrix.…”

Section: A Linear Quadratic Regulator Controlmentioning

confidence: 99%

Design of Optimal Control Techniques for Diesel Engines in Automotive Systems

Ravichandra¹,

K²

2019

IJRAT

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Improving the performance of the state variables associated in the dynamics of air path is a current challenge for the control system community. In the context of automotive systems, disturbance is one of the major factor to destabilize or reduce the performance of the system. Hence, in this paper experiments are performed when the system is subjected to deterministic & random noises by designing the optimal Linear Quadratic Regulator (LQR) and Linear Quadratic Gaussian (LQG) controllers. The proposed controllers are implemented using MATLAB/SIMULINK @ platform and the results are compared.

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Section: A Linear Quadratic Regulator Controlmentioning

confidence: 99%

Design of Optimal Control Techniques for Diesel Engines in Automotive Systems

Ravichandra¹,

K²

2019

IJRAT

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“…To recover the robustness of the LQI, a Loop Transfer Recovery (LTR) involves a fictitious noise source to the input of the plant in Kalman filter design. As the amplitude of injected noise becomes large in intensity, the LQG open-loop transfer function approaches that of the LQR and loop recovery takes place [9][10][11][12] The organization of the paper is the following: mathematical preliminaries including modeling of the satellite dynamics and kinematic are presented in the next section. Section 3 presents the problem formulation and methodology including LQI, LQG and LTR.…”

Section: Introductionmentioning

confidence: 99%

An implementation of optimal control methods (LQI, LQG, LTR) for geostationary satellite attitude control

Djaballah¹,

Mohammed²,

Boughanmi³

2019

IJECE

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This paper investigates a new strategy for geostationary satellite attitude control using Linear Quadratic Gaussian (LQG), Loop Transfer Recovery (LTR), and Linear Quadratic Integral (LQI) control techniques. The sub-system satellite attitude determination and control of a geostationary satellite in the presence of external disturbances, the dynamic model of sub-satellite motion is firstly established by Euler equations. During the flight mission at 35000 Km attitude, the stability characteristics of attitude motion are analyzed with a large margin error of pointing, then a height performance-order LQI, LQG and LTR attitude controller are proposed to achieve stable control of the sub-satellite attitude, which dynamic model is linearized by using feedback linearization method. Finally, validity of the LTR order controller and the advantages over an integer order controller are examined by numerical simulation. Comparing with the corresponding integer order controller (LQI, LQG), numerical simulation results indicate that the proposed sub-satellite attitude controller based on LTR order can not only stabilize the sub-satellite attitude, but also respond faster with smaller overshoot.

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“…As all system states usually are not available, the system state variables are estimated using a KF, forming the LQG controller. The purpose of the studying theory of current controller is to synthesize their control laws with specified proprieties which permit to optimize a performance index and to reduce as well the noises encountered system [16], [17].…”

Section: Introductionmentioning

confidence: 99%

Synthesis of an Optimal Dynamic Regulator Based on Linear Quadratic Gaussian (LQG) for the Control of the Relative Humidity under Experimental Greenhouse

Outanoute¹,

Lachhab²,

Ed-Dahhak³

et al. 2016

IJECE

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This paper describes one practical approach that suggests a model based technique to control in real time the relative humidity under greenhouse. The humidity level is one of the most difficult environmental factors to be regulated in greenhouse. Moreover, maintaining and correcting for more or less humidity can be a challenge for even the most sophisticated monitoring and control equipment. For these raisons, a Linear Quadratic Gaussian (LQG) controller for relative humidity regulation under greenhouse turns out to be useful. Indeed a LQG controller is proposed for a relative humidity under a greenhouse control task. So, the state space model, which is best fitting the acquired data, was identified using the Numerical Subspace State Space System IDentification (N4SID) algorithm. The mathematical model that is obtained will be used for evaluating the parameters of LQG strategy. The proposed controller is implemented in two steps, in one hand, Kalman filter (KF) is used to develop an observer that estimates the state of relative humidity under greenhouse. In the other hand, the state feedback controller gain is estimated using a linear quadratic criterion function. The suggested optimal implemented controller using Matlab/Simulink environment is applied to an experimental greenhouse. We found, according to the results, that the controller is able to lead the inside relative humidity to the desired value with high accuracy, regardless of the external disturbances.

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

Cited by 5 publications

References 8 publications

Design of Optimal Control Techniques for Diesel Engines in Automotive Systems

Design of Optimal Control Techniques for Diesel Engines in Automotive Systems

An implementation of optimal control methods (LQI, LQG, LTR) for geostationary satellite attitude control

Synthesis of an Optimal Dynamic Regulator Based on Linear Quadratic Gaussian (LQG) for the Control of the Relative Humidity under Experimental Greenhouse

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