Journal articles: 'Linear-Quadratic-Gaussian (LQG) controller'

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El Haj, Youssef, and Vijay K. Sood. "Linear Quadratic Gaussian Controller for Single-Ended Primary Inductor Converter via Integral Linear Quadratic Regulator Merged with an Offline Kalman Filter." Energies 17, no. 14 (2024): 3385. http://dx.doi.org/10.3390/en17143385.

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This paper introduces a Linear Quadratic Gaussian (LQG) controller for a Single-Ended Primary Inductor Converter (SEPIC). The LQG design is based on merging an integral Linear Quadratic Regulator (LQR) with an offline Kalman Filter (commonly referred to as a Linear Quadratic Estimator (LQE)). The robustness of the LQG controller is guaranteed based on the separation principle. This manuscript addresses the need to use observer-based systems for the fourth-order SEPIC, which needs a sensor reduction as an essential requirement. This paper provides a comprehensive, yet systematic, approach to designing the LQG system. The work validates the convergences of the states in an LQG system to an actual value. Furthermore, it compares the performance of an LQG system with a benchmark Type-II industrial controller by means of a simulation of the switched converter model in the Simulink/MATLAB 2023a environment.

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Nkemdirim, Chimezirim Miracle, Mohamad Alzayed, and Hicham Chaoui. "Linear Quadratic Gaussian Control of a 6-DOF Aircraft Landing Gear." Energies 16, no. 19 (2023): 6902. http://dx.doi.org/10.3390/en16196902.

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The suspension system of the aircraft, provided by the landing gear, is a crucial part of landing, take-off, and taxiing. It is important that this suspension system not only adequately supports the airframe of the aircraft but also provides a comfortable, seamless ride for the passengers. However, the landing gear is usually riddled with issues, such as landing vibrations that affect passenger comfort and cause damage to the aircraft’s airframe. To reduce these vibrations, this paper proposes the use of a Linear Quadratic Gaussian (LQG) controller to control a 6-DOF aircraft landing gear. The LQG controller is an optimal controller that combines the Linear Quadratic Regulator (LQR) controller with the Kalman filter to compute the system’s control signals and estimate the system’s states. In this paper, the state space model of the 6-DOF landing gear is derived, and the mathematical model of the LQG controller is calculated. The controller’s performance is then tested via MATLAB/Simulink and compared with an equally simple control strategy, the PID controller. The results obtained from the testing process indicate that the LQG controller surpasses the PID controller in reducing landing vibrations, maintaining the aircraft’s airframe, and providing passenger comfort.

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Moellenhoff, D. E., S. Vittal Rao, and C. A. Skarvan. "Design of Robust Controllers for Gas Turbine Engines." Journal of Engineering for Gas Turbines and Power 113, no. 2 (1991): 283–89. http://dx.doi.org/10.1115/1.2906560.

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This paper describes robust controller design methodologies for gas turbine engines. A linear state variable model for the engine is derived using partial derivatives. The Linear Quadratic Gaussian with Loop Transfer Recovery (LQG/LTR) and the Parameter Robust Linear Quadratic Gaussian (PRLQG) robust controller design methodologies have been used to design a controller for gas turbine engines. A new method is proposed by combining the features of LQG/LTR and PRLQG methods and yields good robustness properties with respect to both unstructured uncertainties in the frequency domain and structured parameter variations in the time domain. The new procedure is illustrated with the help of an aircraft gas turbine engine model.

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ATANOV, S. K., and А. Z. BIGALIYEVA. "Synthesis of LQG regulator for intelligent control of the technological process of fine grinding." Bulletin of the National Engineering Academy of the Republic of Kazakhstan 4, no. 78 (2020): 22–27. http://dx.doi.org/10.47533/2020.1606-146x.28.

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The article presents the development of a linear-quadratic Gaussian controller (LQG) for intelligent control of the fine grinding technological process. The LQG regulator was designed to control the quality of mill output. The developed LQG controller takes into account external disturbances (process noise) and noise in measurements modeled as white noise with a Gaussian distribution. The controller is developed on the basis of a combination of a stationary linear quadratic controller (LQR) and estimation of the state of the Kalman filter (LQE) in the stationary state by solving the matrix Riccati equation in order to determine the feedback gain and Kalman gain. In the course of work: a mathematical model of the grinding process is built, an analysis of the frequency characteristics of the obtained model is made; the model was checked for stability, controllability, and observability; on the basis of the model, the LQG regulator was synthesized. Transient process characteristics confirm precise control. Modeling is implemented in the MATLAB environment.

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ATANOV, S. K., and А. Z. BIGALIYEVA. "Synthesis of LQG regulator for intelligent control of the technological process of fine grinding." Bulletin of the National Engineering Academy of the Republic of Kazakhstan 4, no. 78 (2020): 22–27. http://dx.doi.org/10.47533/2020.1606-146x.28.

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The article presents the development of a linear-quadratic Gaussian controller (LQG) for intelligent control of the fine grinding technological process. The LQG regulator was designed to control the quality of mill output. The developed LQG controller takes into account external disturbances (process noise) and noise in measurements modeled as white noise with a Gaussian distribution. The controller is developed on the basis of a combination of a stationary linear quadratic controller (LQR) and estimation of the state of the Kalman filter (LQE) in the stationary state by solving the matrix Riccati equation in order to determine the feedback gain and Kalman gain. In the course of work: a mathematical model of the grinding process is built, an analysis of the frequency characteristics of the obtained model is made; the model was checked for stability, controllability, and observability; on the basis of the model, the LQG regulator was synthesized. Transient process characteristics confirm precise control. Modeling is implemented in the MATLAB environment.

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Zulkarnain, Noraishikin, Hairi Zamzuri, and Saiful Amri Mazlan. "LQG Control Design for Vehicle Active Anti-Roll Bar System." Applied Mechanics and Materials 663 (October 2014): 146–51. http://dx.doi.org/10.4028/www.scientific.net/amm.663.146.

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The objective of this paper is to design a linear quadratic regulator (LQR) and linear quadratic Gaussian (LQG) controllers for an active anti-roll bar system. The use of an active anti-roll bar will be analysed from two different perspectives in vehicle ride comfort and handling performances. This paper proposed the basic vehicle dynamic modelling with four degree of freedom (DOF) on half car model and are described that show, why and how it is possible to control the handling and ride comfort of the car, with the external forces also control strategies on the front anti-roll bar. By simulation analysis, the design model is validity and the performance under control of linear quadratic regulator (LQR) and linear quadratic Gaussian (LQG) controller are achieved. Both two controllers are modeled in MATLAB/SIMULINK environment. It has to be determined which control strategy delivers better performance with respect to roll angle and the roll rate of half vehicle body. The result shows, however, that LQG produced better response compared to a LQR strategy.

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Camino, J. F., and I. F. Santos. "A periodic linear–quadratic controller for suppressing rotor-blade vibration." Journal of Vibration and Control 25, no. 17 (2019): 2351–64. http://dx.doi.org/10.1177/1077546319853358.

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This paper presents an active control strategy, based on a time-varying linear–quadratic optimal control problem, to attenuate the tip vibration of a two-dimensional coupled rotor-blade system whose dynamics is periodic. First, a periodic full-state feedback controller based on the linear–quadratic regulator (LQR) problem is designed. If all the states are not available for feedback, then an optimal periodic time-varying estimator, using the Kalman–Bucy filter, is computed. Both the Kalman filter gain and the LQR gain are obtained as the solution of a periodic Riccati differential equation (PRDE). Together, these gains provide the observer-based linear–quadratic–Gaussian (LQG) controller. An algorithm to solve the PRDE is also presented. Both controller designs ensure closed-loop stability and performance for the linear time-varying rotor-blade equation of motion. Numerical simulations show that the LQR and the LQG controllers are able to significantly attenuate the rotor-blade tip vibration.

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Song, Q., J. Wilkie, and M. J. Grimble. "Robust Controller for Gas Turbines Based Upon LQG/LTR Design With Self-Tuning Features." Journal of Dynamic Systems, Measurement, and Control 115, no. 3 (1993): 569–71. http://dx.doi.org/10.1115/1.2899141.

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A feasibility study is described for the design of a self-tuning controller for gas turbines with the multivariable discrete-time robust controller designed using a Linear Quadratic Gaussian/ Loop Transfer Recovery (LQG/LTR) design approach.

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Farid, Djaballah, Si Mohammed M.A., and Boughanmi Nabil. "An implementation of optimal control methods (LQI, LQG, LTR) for geostationary satellite attitude control." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 6 (2019): 4728–37. https://doi.org/10.11591/ijece.v9i6.pp4728-4737.

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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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Ulsoy, A. G., D. Hrovat, and T. Tseng. "Stability Robustness of LQ and LQG Active Suspensions." Journal of Dynamic Systems, Measurement, and Control 116, no. 1 (1994): 123–31. http://dx.doi.org/10.1115/1.2900666.

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A two-degree-of-freedom quarter-car model is used as the basis for linear quadratic (LQ) and linear quadratic Gaussian (LQG) controller design for an active suspension. The LQ controller results in the best rms performance trade-offs (as defined by the performance index) between ride, handling and packaging requirements. In practice, however, all suspension states are not directly measured, and a Kalman filter can be introduced for state estimation to yield an LQG controller. This paper (i) quantifies the rms performance losses for LQG control as compared to LQ control, and (ii) compares the LQ and LQG active suspension designs from the point of view of stability robustness. The robustness of the LQ active suspensions is not necessarily good, and depends strongly on the design of a backup passive suspension in parallel with the active one. The robustness properties of the LQG active suspension controller are also investigated for several distinct measurement sets.

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MIRZA, K. Z. "Linear Quadratic Gaussian Regulator for the Nonlinear Observer-Based Control of a Dynamic Base Inverted Pendulum." INCAS BULLETIN 13, no. 3 (2021): 79–90. http://dx.doi.org/10.13111/2066-8201.2021.13.3.7.

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The inverted pendulum is a non-linear control problem permanently tending towards instability. The main aim of this study is to design a controller capable enough to work within the given conditions while also keeping the pendulum erect given the impulsive movement of the cart to which it is joint via a hinge. The first half of the paper presents the mathematical modelling of the dynamic system, together with the design of a linear quadratic regulator (LQR). This paper also discusses a novel adaptive control mechanism employing a Kalman filter for the mobile inverted pendulum system (MIPS). In the second half of the paper, a Gaussian Quadratic Linear Controller (LQG) is adapted to improve on previous deficiencies. The simulation is done through Simulink and results show that both controllers are capable of managing the multiple output model. However, data from simulations clearly showed that an LQG controller is a better choice.

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Ingabire, Aline, and Andrey A. Sklyarov. "Control of longitudinal flight dynamics of a fixedwing UAV using LQR, LQG and nonlinear control." E3S Web of Conferences 104 (2019): 02001. http://dx.doi.org/10.1051/e3sconf/201910402001.

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This paper aim is to present a comparative study between Linear Quadratic Regulator (LQR), Linear Quadratic Gaussian (LQG) and nonlinear controllers for pitch control of a fixed-wing Unmanned Aerial Vehicle (UAV). Due to a good stability margin and strong robustness LQR has been selected. LQG was chosen because is able to overcome external disturbances. Kalman Filter controller was also introduced to the fixed-wing UAV flight control. Further, we designed an autopilot that controls the pitch angle of the fixed-wing UAV. In the end, the control laws are simulated in Matlab/Simulink. The results obtained are compared to see which method is faster, more reliable and more robust.

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Djaballah, Farid, M. A. Si Mohammed, and Nabil Boughanmi. "An implementation of optimal control methods (LQI, LQG, LTR) for geostationary satellite attitude control." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 6 (2019): 4728. http://dx.doi.org/10.11591/ijece.v9i6.pp4728-4737.

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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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Mohammed, Ibrahim Khalaf, and Mohanad N. Noaman. "Optimal Control Approach for Robot System Using LQG Technique." Journal Européen des Systèmes Automatisés 55, no. 5 (2022): 671–77. http://dx.doi.org/10.18280/jesa.550513.

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A two-wheeled self-balancing robot system bases on the physical problem of an inverted pendulum. Stabilization of this type of mobile robot requires applying an active control approach. This paper proposes an efficient Linear Quadratic Gaussian (LQG) optimal control for the two-wheeled robot system. The LQG (a combination of a Kalman Filter (KF) and Linear Quadratic Regulator (LQR)) controller is designed to stabilize the robot while reducing the effect of the process and measurement noises on its performance. The LQG controller parameters (elements of state and control weighting matrices of the LQR and KF) are optimally tuned using the Particle Swarm Optimization (PSO) optimization method. The robot stabilization scheme is simulated utilizing MATLAB software to validate the proposed PSO-LQG controller system. The effectiveness of the proposed controller is validated based on the control criteria parameters, which are rise time, settling time, maximum overshoot, and steady-state error. The results prove that the proposed PSO-LQG controller can give very good movement performance in terms of both transient and steady-state responses.

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Bigaliyeva, A. Z. "Development of linear-quadratic-gaussian control of the technological process of fine grinding." Bulletin of the National Engineering Academy of the Republic of Kazakhstan 95, no. 1 (2025): 60–71. https://doi.org/10.47533/2025.1606-146x.04.

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The paper is dedicated to research of grinding process in a planetary ball mill. The possibility of continuous control of fineness of grinding with the usage of optimization methods is taken into consideration. The mathematical representation of an object had been built to consider it as the mathematical model. The comparison of given model to data of natural experiments is performed. In the paper, the results of analysis of the main quality points from mathematical model are included. On the model base the linear- quadratic regulator LQG is synthesized, which represents the combination of the Kalman filter The Linear Quadratic Estimation (LQE) along with The Linear Quadratic Estimation (LQR). The regulator is used to achieve the main task – keeping up of strictly defined range in predefined class output by controlling incoming flow in the ball mill. In the paper general enunciation of Linear Quadratic Gaussian (LQG) is described along with the engineering procedures and all necessary hypothesis. The obtained results from modeling of LQG controller are shown.

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Mitov, Alexander, Jordan Kralev, Tsonyo Slavov, and Ilcho Angelov. "Comparison of Model Predictive Control (MPC) and Linear-Quadratic Gaussian (LQG) Algorithm for Electrohydraulic Steering Control System." E3S Web of Conferences 207 (2020): 04001. http://dx.doi.org/10.1051/e3sconf/202020704001.

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The paper compares the performance of two embedded controllers applied in electrohydraulic steering systems – model predictive controller (MPC) and linear-quadratic Gaussian (LQG) controller with Kalman filtering for state estimation. Both controllers are designed on the basis of single input multiple output “black box” model obtained via identification approach. The controllers are implemented into industrial logic controller for mobile applications and their workability is experimentally checked with a laboratory model of a steering system for non-road mobile machinery. The results corresponding to investigation of performance of the closed-loop system are presented.

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Kim, Young Moon, Ki Pyo You, and Jang Youl You. "LQG Control of Across-Wind Response of a Tall Building with AMD." Advanced Materials Research 1004-1005 (August 2014): 1602–7. http://dx.doi.org/10.4028/www.scientific.net/amr.1004-1005.1602.

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Modern tall buildings are more flexible so occur excessive wind-induced vibration resulting in occupant discomfort and structural safety. Many studies to reduce such a wind-induced vibration using a feedback controller and auxiliary devices have been conducted .The optimal control law of linear quadratic Gaussian (LQG) controller is used for reducing the across-wind vibration response of a tall building with an active mass damper (AMD). Fluctuating across-wind load treated as a Gaussian white noise process is simulated numerically in time domain. And using this simulated across-wind load estimated across-wind vibration responses of tall building with AMD using LQG controller.

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Tayebi, Javad, Amir Ali Nikkhah, and Jafar Roshanian. "LQR/LQG attitude stabilization of an agile microsatellite with CMG." Aircraft Engineering and Aerospace Technology 89, no. 2 (2017): 290–96. http://dx.doi.org/10.1108/aeat-07-2014-0102.

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Purpose The purpose of the paper is to design a new attitude stabilization system for a microsatellite based on single gimbal control moment gyro (SGCMG) in which the gimbal rates are selected as controller parameters. Design/methodology/approach In the stability mode, linear quadratic regulator (LQR) and linear quadratic Gaussian (LQG) control strategies are presented with the gimbal rates as a controller parameters. Instead of developing a control torque to solve the attitude problem, the attitude controller is developed in terms of the control moment gyroscope gimbal angular velocities. Attitude control torques are generated by means of a four SGCMG pyramid cluster. Findings Numerical simulation results are provided to show the efficiency of the proposed controllers. Simulation results show that this method could stabilize satellite from initial condition with large angles and with more accuracy in comparison with feedback quaternion and proportional-integral-derivative controllers. These results show the effect of filtering the noisy signal in the LQG controller. LQG in comparison to LQR is more realistic. Practical implications The LQR method is more appropriate for the systems that have project models reasonably exact and ideal sensors/actuators. LQG is more realistic, and it can be used when not all of the states are available or when the system presents noises. LQR/LQG controller can be used in the stabilization mode of satellite attitude control. Originality/value The originality of this paper is designing a new attitude stabilization system for an agile microsatellite using LQR and LQG controllers.

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Muddasar, Ali, Tahreem Zahra Syeda, Jalal Khadija, Saddiqa Ayesha, and Faisal Hayat Muhammad. "Design of Optimal Linear Quadratic Gaussian (LQG) Controller for Load Frequency Control (LFC) using Genetic Algorithm (G.A) in Power System." International Journal of Engineering Works (ISSN: 2409-2770) 5, no. 3 (2018): 40–49. https://doi.org/10.5281/zenodo.1193795.

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Nowadays power demand is increasing continuously and the biggest challenge for the power system is to provide good quality of power to the consumer under changing load conditions. When real power changes, system frequency gets affected while reactive power is dependent on variation in voltage value. For satisfactory operation the frequency of power system should be kept near constant value. Many techniques have been proposed to obtain constant value of frequency and to overcome any deviations. The Load Frequency Control (LFC) is used to restore the balance between load and generation by means of speed control. The main goal of LFC is to minimize the frequency deviations to zero. LFC incorporates an appropriate control system which is having the capability to bring the frequency of the Power system back to original set point values or very near to set point values effectively after the load change. This can be achieved by using a conventional controller like PID but the conventional controller is very slow in operation. Modern and optimal controllers are much faster and they also give better output response than conventional controllers. Linear Quadratic Regulator (LQR) is an advanced control technique in feedback control systems. It’s a control strategy based on minimizing a quadratic performance index. In despite of good results obtained from this method, the control design is not a straight forward task due to the trial and error involved in the selection of weight matrices Q and R. In this case, it may be hard to tune the controller parameters to obtain the optimal behaviour of the system. The difficulty to determine the weight matrices Q and R in LQR controller is solved using Genetic Algorithm (G.A).  In this research Paper, G.A based LQG controller which is the combination of LQR and Kalman Filter is feedback in LFC using MATLAB/SIMULINK software package. Reduction in frequency deviations and settling time was successfully achieved by using LQG Controller with LFC based on G.A.

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Huynh, Phuc-Hoang, Cong-Duy Pham, Nam-Binh Vu, et al. "A Survey of LQG over MPC and LQR Control for Rotary Inverted Pendulum." Robotica & Management 29, no. 2 (2024): 10–15. https://doi.org/10.24193/rm.2024.2.2.

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In this paper, we examine the theoretical cost function equivalence between Model Predictive Control (MPC) and Linear-Quadratic Gaussian (LQG) control, as well as Linear-Quadratic Regulator (LQR) control under specific conditions. Specifically, we linearize the Rotary Inverted Pendulum (RIP) system and construct a Kalman filter state estimator for application in both the LQG and MPC controllers with input and output constraints. We also assume measurable and computable states when designing the LQR controller. Through simulation and experimentation, we demonstrate that, despite the equivalence in cost functions, the output response of MPC is significantly better than that of both LQG and LQR. Our findings not only substantially bridge gaps in control theory but also emphasize the robustness of MPC in complex real-world applications. These insights pave the way for more effective and reliable control strategies across various engineering fields.

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Sivrioglu, Selim, and Fevzi Cakmak Bolat. "Switching linear quadratic Gaussian control of a flexible blade structure containing magnetorheological fluid." Transactions of the Institute of Measurement and Control 42, no. 3 (2019): 618–27. http://dx.doi.org/10.1177/0142331219878956.

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The present study suggests a new active control structure with a switching algorithm for control current of the electromagnetic actuator to suppress vibrations of a flexible blade element containing magnetorheological (MR) fluid. An identification model between MR fluid inside the blade and the electromagnetic actuator is utilized to derive the force equation. Since only one electromagnet is employed to suppress the blade vibration, a single-sided control actuation occurs in the proposed system. Therefore, the pulling force used for vibration control is generated by the actuator when the blade moves to downward direction. A switching linear quadratic Gaussian (LQG) controller that produces always a positive control input is designed to attenuate the blade vibrations. The LQG controller with switching is experimentally implemented under the impact and steady-state aerodynamic disturbances. The results of experiments show that the proposed switching controller is effective to attenuate vibrations of the flexible blade element.

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Li, Jin Hui, Jie He, and Xu Hong Li. "LQG Controller Design for Heavy Vehicle Active Suspension Based on OT-AHP Method." Applied Mechanics and Materials 509 (February 2014): 206–12. http://dx.doi.org/10.4028/www.scientific.net/amm.509.206.

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In order to develop more scientific LQG (Linear-Quadratic Gaussian) optimal controller of vehicle active suspension, based on vehicle systme dynamics and modern control theory, the LQG controller of one 4-dof active suspension vehicle model was presented. And the index weighting coefficiens in LQG controller were rationally determind by AHP (Analytic Hierarchy Process) method. Based on this, for furtherly improving vehicle performanc, the subjective important degrees in AHP method were optimized by OT (Othogonal Test) method which took the linear weighted sum of ride comfort and road friendliness index as the comprehensive performance evaluation index. The simulation results showed that, compared to AHP method, the LQG active suspension vehicle designed by OT-AHP (Orthogonal Test of Analytic Hierarchy Process) method could have better comprehensive performance, which could provide new ideas for the development of vehicle active suspension.

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Ma, Shu Yuan, Bdran Sameh, Saifullah Samo, and Aymn Bary. "Improvement Shifting Control of Continuously Variable Transmission (CVT) by Using PID, Pole Placement and LQG." Applied Mechanics and Materials 446-447 (November 2013): 1165–70. http://dx.doi.org/10.4028/www.scientific.net/amm.446-447.1165.

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In this paper, the CVT shifting control system based on vehicle operating conditions is modeled and simulated using MATLAB/SIMULINK. The modeling stage begins with the derivation of required mathematical model to illustrate the CVT shifting control system. Then, Linear Quadratic Gaussian (LQG), Proportional- Integrated-Derivative (PID) and Pole Placement are applied for controlling the shifting speed ratio of the modeled CVT shifting system. Simulation results of shifting controllers are presented in time domain and the results obtained with LQG are compared with the results of PID and Pole placement technique. Finally, the performances of shifting speed ratio controller systems are analyzed in order to choose which control method offers the better performance with respect to the desired speed ratio. According to simulation results, the LQG controller delivers better performance than PID and Pole Placement controller.

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Lai, Hoan Bao, Anh-Tuan Tran, Van Huynh, Emmanuel Nduka Amaefule, Phong Thanh Tran, and Van-Duc Phan. "Optimal linear quadratic Gaussian control based frequency regulation with communication delays in power system." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 1 (2022): 157. http://dx.doi.org/10.11591/ijece.v12i1.pp157-165.

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In this paper, load frequency regulator based on linear quadratic Gaussian (LQG) is designed for the MAPS with communication delays. The communication delay is considered to denote the small time delay in a local control area of a wide-area power system. The system is modeled in the state space with inclusion of the delay state matrix parameters. Since some state variables are difficult to measure in a real modern multi-area power system, Kalman filter is used to estimate the unmeasured variables. In addition, the controller with the optimal feedback gain reduces the frequency spikes to zero and keeps the system stable. Lyapunov function based on the LMI technique is used to re-assure the asymptotically stability and the convergence of the estimator error. The designed LQG is simulated in a two area connected power network with considerable time delay. The result from the simulations indicates that the controller performed with expectation in terms of damping the frequency fluctuations and area control errors. It also solved the limitation of other controllers which need to measure all the system state variables.

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Hoan, Bao Lai, Tran Anh-Tuan, Huynh Van, Nduka Amaefule Emmanuel, Thanh Tran Phong, and Phan Van-Duc. "Optimal linear quadratic Gaussian control based frequency regulation with communication delays in power system." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 1 (2022): 157–65. https://doi.org/10.11591/ijece.v12i1.pp157-165.

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In this paper, load frequency regulator based on linear quadratic Gaussian (LQG) is designed for the multi-area power system (MAPS) with communication delays. The communication delay is considered to denote the small time delay in a local control area of a wide-area power system. The system is modeled in the state space with inclusion of the delay state matrix parameters. Since some state variables are difficult to measure in a real modern multi-area power system, Kalman filter is used to estimate the unmeasured variables. In addition, the controller with the optimal feedback gain reduces the frequency spikes to zero and keeps the system stable. Lyapunov function based on the linear matrix inequality (LMI) technique is used to re-assure the asymptotically stability and the convergence of the estimator error. The designed LQG is simulated in a two area connected power network with considerable time delay. The result from the simulations indicates that the controller performed with expectation in terms of damping the frequency fluctuations and area control errors. It also solved the limitation of other controllers which need to measure all the system state variables.

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White, Langford B., and Belinda A. Chiera. "An adaptive LQG TCP congestion controller for the Internet." Journal of Telecommunications and Information Technology, no. 1 (March 30, 2006): 30–37. http://dx.doi.org/10.26636/jtit.2006.1.360.

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This paper addresses the problem of congestion control for transmission control protocol (TCP) traffic in the Internet. The method proposed builds on the ideas of TCP Vegas, a true feedback control approach to congestion management of TCP traffic. The new method is based on an adaptive linear quadratic Gaussian (LQG) formulation which uses an extended least squares system identification algorithm com- bined with optimal LQG control. Simulation experiments indicate that the new technique inherits good equilibrium properties from TCP Vegas, but has much superior transient responses which, the paper argues, is important for good dynamic congestion control.

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Karim, Selam, Allaoui Tayeb, and Tadjine Mohamed. "LQG Controller for the Control of Active and Reactive Power of DFIG Operating Under Inter-turn Short Circuit Fault." Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) 13, no. 3 (2020): 340–47. http://dx.doi.org/10.2174/2352096511666181114120443.

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Objective: This article presents a comparative study between the direct control for a Doubly- Fed Induction Generator (DFIG) in the healthy and faulty mode. Methods: First with classical IP controller then Linear Quadratic Gaussian (LQG) controller which propose an ensemble of Linear Quadratic Regulator and Kalman filter for the state estimation. The developed model of the machine allows the simulation of the inter-turn short circuit in the stator. The use of the LQG method provides very good performance for motor operation and robustness of the control law despite the external perturbation. The performance of the control is compared to a classical controller's PI. Results: The obtained results demonstrate that this type of controller allows the alleviation of the mechanical stress and it ensures good performances under fault, the continuity of this system is ensured. Conclusion: The simulation has been carried out using a MATLAB script and the results are presented.

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Plummer, A. R. "A comparison of linear discrete-time design methods for servosystem control." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 211, no. 4 (1997): 281–300. http://dx.doi.org/10.1243/0959651971539812.

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Three linear discrete-time model-based controller design techniques are compared: pole placement, linear quadratic Gaussian (LQG) and H∞ control. It is shown that design choices can be made for all three controllers by considering the effect on the sensitivity functions of the closed-loop system. Also all three controllers can be implemented using an identical controller structure. A comparative study of the application of the techniques to an electromechanical servosystem is made. The controllers are designed from a discrete-time plant model estimated from experimental data, and a polynomial-based solution method is used in each case. It is concluded that acceptable performance can be achieved using any of the controllers if informed design choices are made.

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Bialic, Grzegorz, and Rafał Stanisławski. "On the Linear–Quadratic–Gaussian Control Strategy for Fractional-Order Systems." Fractal and Fractional 6, no. 5 (2022): 248. http://dx.doi.org/10.3390/fractalfract6050248.

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In the paper, the Linear–Quadratic–Gaussian (LQG) control strategy in regulatory mode (disturbance attenuation, zero value of the reference signal) in single-loop control is used to stabilize the system equipped in a non-integer order plant. The influence of the optimal controller design sophistication on control quality in terms of output variance is examined. It has been shown that the optimal implementation length of fractional-order difference is relatively low (several dozen in considered examples). Therefore, further increasing the controller’s complexity in terms of approximation length does not improve the control performance. Furthermore, it is presented that, under bounded control signal variance, the optimal fractional order of the controller may be significantly different from the actual fractional order of the plant (in the examples, the difference is up to 0.66).

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OR, A. C., L. CORTELEZZI, and J. L. SPEYER. "Robust feedback control of Rayleigh–Bénard convection." Journal of Fluid Mechanics 437 (June 22, 2001): 175–202. http://dx.doi.org/10.1017/s0022112001004256.

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We investigate the application of linear-quadratic-Gaussian (LQG) feedback control, or, in modern terms, [Hscr ]2 control, to the stabilization of the no-motion state against the onset of Rayleigh–Bénard convection in an infinite layer of Boussinesq fluid. We use two sensing and actuating methods: the planar sensor model (Tang & Bau 1993, 1994), and the shadowgraph model (Howle 1997a). By extending the planar sensor model to the multi-sensor case, it is shown that a LQG controller is capable of stabilizing the no-motion state up to 14.5 times the critical Rayleigh number. We characterize the robustness of the controller with respect to parameter uncertainties, unmodelled dynamics. Results indicate that the LQG controller provides robust performances even at high Rayleigh numbers.

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Kadhem, Basim. "Using a Reduced Order Robust Control Approach to Damp Subsynchronous Resonance in Power Systems." Iraqi Journal for Electrical and Electronic Engineering 19, no. 1 (2022): 29–37. http://dx.doi.org/10.37917/ijeee.19.1.4.

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his work focuses on the use of the Linear Quadratic Gaussian (LQG) technique to construct a reliable Static VAr Compensator (SVC), Thyristor Controlled Series Compensator (TCSC), and Excitation System controller for damping Subsynchronous Resonance ( SSR ) in a power system. There is only one quantifiable feedback signal used by the controller (generator speed deviation). It is also possible to purchase this controller in a reduced-order form. The findings of the robust control are contrasted with those of the “idealistic” full state optimal control. The LQG damping controller’s regulator robustness is then strengthened by the application of Loop Transfer Recovery (LTR). Nonlinear power system simulation is used to confirm the resilience of the planned controller and demonstrates how well the regulator dampens power system oscillations. The approach dampens all torsional oscillatory modes quickly while maintaining appropriate control actions, according to simulation results.

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Sawicki, J. T., and W. K. Gawronski. "Balanced Model Reduction and Control of Rotor-Bearing Systems." Journal of Engineering for Gas Turbines and Power 119, no. 2 (1997): 456–63. http://dx.doi.org/10.1115/1.2815596.

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An effective technique is applied to the suppression of vibrations in flexible rotor-bearing systems with small gyroscopic effects. A balanced linear-quadratic-Gaussian (LQG) controller design procedure is implemented. The size of the controller is reduced in two stages by using (i) a balanced model reduction, and (ii) an LQG balanced reduction. The condition for a gyroscopic matrix is developed that allows one to ignore the rotor gyroscopic effects in the process of the controller design, although they are included in the rotor dynamics. The approach is illustrated on a typical rotor-bearing system represented by a 48 degree-of-freedom finite element model.

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Sohn, Jung Woo, Heung Soo Kim, and Seung Bok Choi. "Dynamic Characteristics of Smart Hull Structures Featuring Piezoelectric Materials." Key Engineering Materials 306-308 (March 2006): 1145–50. http://dx.doi.org/10.4028/www.scientific.net/kem.306-308.1145.

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In this paper, dynamic characteristics of an end-capped hull structure with surface bonded piezoelectric actuators are studied. Finite element technique is used to ensure application to practical geometry and boundary conditions of smart hull structure. Modal analysis is conducted to investigate the dynamic characteristics of the hull structure. Piezoelectric self-sensing actuators are attached where the maximum control performance can be obtained. Active controller based on Linear Quadratic Gaussian (LQG) theory is designed to suppress vibration of smart hull structure. It is observed that closed loop damping can be improved with suitable weighting factors in the developed LQG controller.

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Sopegno, Laura, Patrizia Livreri, Margareta Stefanovic, and Kimon P. Valavanis. "Thrust Vector Controller Comparison for a Finless Rocket." Machines 11, no. 3 (2023): 394. http://dx.doi.org/10.3390/machines11030394.

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The paper focuses on comparing applicability, tuning, and performance of different controllers implemented and tested on a finless rocket during its boost phase. The objective was to evaluate the advantages and disadvantages of each controller, such that the most appropriate one would then be developed and implemented in real-time in the finless rocket. The compared controllers were Linear Quadratic Regulator (LQR), Linear Quadratic Gaussian (LQG), and Proportional Integral Derivative (PID). To control the attitude of the rocket, emphasis is given to the Thrust Vector Control (TVC) component (sub-system) through the gimballing of the rocket engine. The launcher is commanded through the control input thrust gimbal angle δ, while the output parameter is expressed in terms of the pitch angle θ. After deriving a linearized state–space model, rocket stability is addressed before controller implementation and testing. The comparative study showed that both LQR and LQG track pitch angle changes rapidly, thus providing efficient closed-loop dynamic tracking. Tuning of the LQR controller, through the Q and R weighting matrices, illustrates how variations directly affect performance of the closed-loop system by varying the values of the feedback gain (K). The LQG controller provides a more realistic profile because, in general, not all variables are measurable and available for feedback. However, disturbances affecting the system are better handled and reduced with the PID controller, thus overcoming steady-state errors due to aerodynamic and model uncertainty. Overall controller performance is evaluated in terms of overshoot, settling and rise time, and steady-state error.

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Sheng, Yan, Chao Wang, Ying Pan, and Xinhua Zhang. "Modified LQG/LTR Control Methodology in Active Structural Control." Journal of Low Frequency Noise, Vibration and Active Control 22, no. 2 (2003): 97–108. http://dx.doi.org/10.1260/026309203322770347.

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This paper presents a new active structural control design methodology comparing the conventional linear-quadratic-Gaussian synthesis with a loop-transfer-recovery (LQG/LTR) control approach for structures subjected to ground excitations. It results in an open-loop stable controller. Also the closed-loop stability can be guaranteed. More importantly, the value of the controller's gain required for a given degree of LTR is orders of magnitude less than what is required in the conventional LQG/LTR approach. Additionally, for the same value of gain, the proposed controller achieves a much better degree of recovery than the LQG/LTR-based controller. Once this controller is obtained, the problems of control force saturation are either eliminated or at least dampened, and the controller band-width is reduced and consequently the control signal to noise ratio at the input point of the dynamic system is increased. Finally, numerical examples illustrate the above advantages.

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Montenegro-Oviedo, Jhoan Alejandro, Carlos Andres Ramos-Paja, Martha Lucia Orozco-Gutierrez, Edinson Franco-Mejía, and Sergio Ignacio Serna-Garcés. "Experimental Design of an Adaptive LQG Controller for Battery Charger/Dischargers Featuring Low Computational Requirements." World Electric Vehicle Journal 14, no. 6 (2023): 142. http://dx.doi.org/10.3390/wevj14060142.

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The growing use of DC/DC power converters has resulted in the requirement that their complex controllers be cheaper and smaller, thus using cost-effective implementations. For this purpose, it is necessary to decrease the computational burden in controller implementation to minimize the hardware requirements. This manuscript presents two methods for tuning an adaptive linear–quadratic–Gaussian voltage controller for a battery charger/discharger, implemented with a Sepic/Zeta converter, to work at any operating point. The first method is based on a lookup table to select, using the nearest method, both the state feedback vector and the observer gain vector, solving the Riccati’s differential equation offline for each practical operating point. The second method defines a polynomial function for each controller element that is based on the previous data corresponding to the system operating points. The adaptability of the two controllers to fixed voltage regulation and reference tracking was validated using simulations and experimental tests. The overshoot and settling time results were lower than 11% and 3.7 ms, which are in the same orders of magnitude of a control approach in which the equations are solved online. Likewise, three indices were evaluated: central processing unit capacity, cost, and performance. This evaluation confirms that the controller based on polynomial interpolation is the best option of the two examined methods due to the satisfactory balance between dynamic performance and cost. Despite the advantages of the controllers in being based on a lookup table and polynomial interpolation, the adaptive linear–quadratic–Gaussian has the benefit of not requiring an offline training campaign; however, the cost saving obtained with the lookup table controllers and polynomial interpolation controllers, due to the possible implementation on small-size microcontrollers with development tool simple and easy maintenance, will surely be desirable for a large number of deployed units, ensuring that those solutions are highly cost-effective.

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Pang, Hui, Ying Chen, JiaNan Chen, and Xue Liu. "Design of LQG Controller for Active Suspension without Considering Road Input Signals." Shock and Vibration 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/6573567.

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As the road conditions are completely unknown in the design of a suspension controller, an improved linear quadratic and Gaussian distributed (LQG) controller is proposed for active suspension system without considering road input signals. The main purpose is to optimize the vehicle body acceleration, pitching angular acceleration, displacement of suspension system, and tire dynamic deflection comprehensively. Meanwhile, it will extend the applicability of the LQG controller. Firstly, the half-vehicle and road input mathematical models of an active suspension system are established, with the weight coefficients of each evaluating indicator optimized by using genetic algorithm (GA). Then, a simulation model is built in Matlab/Simulink environment. Finally, a comparison of simulation is conducted to illustrate that the proposed LQG controller can obtain the better comprehensive performance of vehicle suspension system and improve riding comfort and handling safety compared to the conventional one.

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Jeong, Seok Kwon, and Tae Eun Kwon. "Robust Linear Quadratic Gaussian Controller Design for Oil Coolers Based on a State Space Model." Korean Journal of Air-Conditioning and Refrigeration Engineering 31, no. 3 (2019): 130–39. http://dx.doi.org/10.6110/kjacr.2019.31.3.130.

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DONG, X. M., MIAO YU, S. L. HUANG, ZUSHU LI, and W. M. CHEN. "HALF CAR MAGNETORHEOLOGICAL SUSPENSION SYSTEM ACCOUNTING FOR NONLINEARITY AND TIME DELAY." International Journal of Modern Physics B 19, no. 07n09 (2005): 1381–87. http://dx.doi.org/10.1142/s0217979205030335.

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MR suspension systems have significant non-linearity and time-delay characteristics. For this reason, linear feedback control of an MR suspension has limited vibration control performance. To address this problem, a four DOF half car suspension model with two MR dampers was adopted. Having analyzed non-linearity and time-delay of the MR suspension, a Human-Simulation Intelligent Control (HSIC) law with three levels was designed. Simulation verified effects of HSIC in solving the problem of non-linearity and time-delay of MR dampers. In comparison, simulation of linear-quadratic gaussian (LQG) without considering the non-linearity and time-delay of MR suspension is also made. The simulation results show that the HSIC controller is faster than LQG controller under bump input and has better stability and accuracy, and it can achieve smaller acceleration peak value and root mean square (RMS) and better ride comfort compared with LQG controller under random input.

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Wang, Wei, Hao Ma, Min Xia, Liguo Weng, and Xuefei Ye. "Attitude and Altitude Controller Design for Quad-Rotor Type MAVs." Mathematical Problems in Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/587098.

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Micro air vehicles (MAVs) have a wide application such as the military reconnaissance, meteorological survey, environmental monitoring, and other aspects. In this paper, attitude and altitude control for Quad-Rotor type MAVs is discussed and analyzed. For the attitude control, a new method by using three gyroscopes and one triaxial accelerometer is proposed to estimate the attitude angle information. Then with the approximate linear model obtained by system identification, Model Reference Sliding Mode Control (MRSMC) technique is applied to enhance the robustness. In consideration of the relatively constant altitude model, a Linear Quadratic Gaussian (LQG) controller is adopted. The outdoor experimental results demonstrate the superior stability and robustness of the controllers.

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Outanoute, M., A. Lachhab, A. Ed-dahhak, M. Guerbaoui, A. Selmani, and B. Bouchikhi. "Synthesis of an Optimal Dynamic Regulator Based on Linear Quadratic Gaussian (LQG) for the Control of the Relative Humidity under Experimental Greenhouse." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 5 (2016): 2262. http://dx.doi.org/10.11591/ijece.v6i5.10470.

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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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Outanoute, M., A. Lachhab, A. Ed-dahhak, M. Guerbaoui, A. Selmani, and B. Bouchikhi. "Synthesis of an Optimal Dynamic Regulator Based on Linear Quadratic Gaussian (LQG) for the Control of the Relative Humidity under Experimental Greenhouse." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 5 (2016): 2262. http://dx.doi.org/10.11591/ijece.v6i5.pp2262-2273.

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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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Li, Wen Bo, Xiao Ran Li, Zhi Gang Zhao, You Yi Wang, and Yang Zhao. "Optimal Piezoelectric Sensors and Actuators Deployment for Active Vibration Suppression of Satellite Antenna Reflector." Advanced Materials Research 479-481 (February 2012): 1490–94. http://dx.doi.org/10.4028/www.scientific.net/amr.479-481.1490.

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To solve the problem of active vibration control for satellite antenna reflector, which is weak damping and closely spaced modes, the optimal actuators/sensors deployment and controller designing need to be considered. Firstly, the optimal criterions of controllability and observability are designed according to the specificity of Gram Matrix eigenvalue in satellite antenna system equations. Secondly, based on the above criterions, piezoelectric materials (as sensors and actuators) and genetic algorithm are utilized to optimize the deployed locations of sensors and actuators. Finally, to suppress the vibration of satellite antenna reflector, a Linear Quadratic Gaussian (LQG) controller is designed under the impulse and white noise excitation respectively. The simulate results show the effectively deployed locations of sensors and actuators, and the correctness of designed LQG controller.

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Sohn, J. W., H. S. Kim, and S. B. Choi. "Active vibration control of smart hull structures using piezoelectric actuators." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 220, no. 9 (2006): 1329–37. http://dx.doi.org/10.1243/09544062c06105.

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In this study, dynamic characteristics of an end-capped hull structure with surfacebonded piezoelectric actuators are studied and active vibration controller is designed to suppress the undesired vibration of the structure. Finite-element modelling is used to obtain practical governing equation of motion and boundary conditions of smart hull structure. A modal analysis is conducted to investigate the dynamic characteristics of the hull structure. Piezoelectric actuators are attached where the maximum control performance can be obtained. Active controller on the basis of a linear quadratic Gaussian (LQG) theory is designed to suppress the vibration of smart hull structure. It is observed that closed-loop damping can be improved with suitable weighting factors in the developed LQG controller and the structural vibration can be successfully suppressed.

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Ahn, Da-Vin, Kyeongdae Kim, Jooseon Oh, Jaho Seo, Jin Woong Lee, and Young-Jun Park. "Optimal Control of Semi-Active Suspension for Agricultural Tractors Using Linear Quadratic Gaussian Control." Sensors 23, no. 14 (2023): 6474. http://dx.doi.org/10.3390/s23146474.

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In this study, a semi-active suspension based on a hydro-pneumatic mechanism was designed to minimize the ride vibration using a suspension control algorithm. The performance of the algorithm was critical for controlling the characteristics of the target tractor. A linear-quadratic-Gaussian (LQG) optimal control algorithm was designed as a semi-active suspension control algorithm. The plant model for developing this algorithm was based on the parameters of an actual tractor. The rear suspension deflection was represented by a Kalman-filter-based state observer feedback to estimate the state variables that were difficult to measure. The designed state observer of the LQG controller was validated in terms of an accuracy index. The estimated vertical velocity and acceleration accuracies of the cabin were 83% and 79%, respectively. The performance of the designed controller was validated in terms of a performance index by comparing the performance of a tractor equipped with a rear rubber mount with that of one equipped with a semi-active suspension. The peak and root-mean-square values of the vertical acceleration of the cabin were reduced by up to 48.97% and 47.06%, respectively. This study could serve as a basis for the application of the control algorithm to systems with similar characteristics, thereby reducing system costs.

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Tottori, Takehiro, and Tetsuya J. Kobayashi. "Decentralized Stochastic Control with Finite-Dimensional Memories: A Memory Limitation Approach." Entropy 25, no. 5 (2023): 791. http://dx.doi.org/10.3390/e25050791.

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Decentralized stochastic control (DSC) is a stochastic optimal control problem consisting of multiple controllers. DSC assumes that each controller is unable to accurately observe the target system and the other controllers. This setup results in two difficulties in DSC; one is that each controller has to memorize the infinite-dimensional observation history, which is not practical, because the memory of the actual controllers is limited. The other is that the reduction of infinite-dimensional sequential Bayesian estimation to finite-dimensional Kalman filter is impossible in general DSC, even for linear-quadratic-Gaussian (LQG) problems. In order to address these issues, we propose an alternative theoretical framework to DSC—memory-limited DSC (ML-DSC). ML-DSC explicitly formulates the finite-dimensional memories of the controllers. Each controller is jointly optimized to compress the infinite-dimensional observation history into the prescribed finite-dimensional memory and to determine the control based on it. Therefore, ML-DSC can be a practical formulation for actual memory-limited controllers. We demonstrate how ML-DSC works in the LQG problem. The conventional DSC cannot be solved except in the special LQG problems where the information the controllers have is independent or partially nested. We show that ML-DSC can be solved in more general LQG problems where the interaction among the controllers is not restricted.

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Joshi, Suresh M. "Controller Design and Parameter Identifiability Studies for a Large Space Antenna." Transactions of the Canadian Society for Mechanical Engineering 9, no. 3 (1985): 125–30. http://dx.doi.org/10.1139/tcsme-1985-0018.

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The problem of control systems synthesis and parameter identifiability are considered for a large, flexible, space-based antenna. Two methods are considered for control system synthesis, the first of which uses torque actuators and collocated attitude and rate sensors, and the second method is based on the linear-quadratic-Gaussian (LQG) control theory. The predicted performance obtained by computing variances of pointing, surface and feed misalignment errors in the presence of sensor noise indicates that the LQG-based controller yields superior results. Since controller design requires the knowledge of the system parameters, the identifiability of the structural parameters is investigated by obtaining Cramér-Rao lower bounds. The modal frequencies are found to have the best identifiability, followed by damping ratios, and mode-slopes.

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Wang, Yong, Qiangang Zheng, Haibo Zhang, and Mingyang Chen. "The LQG/LTR control method for turboshaft engine with variable rotor speed based on torsional vibration suppression." Journal of Low Frequency Noise, Vibration and Active Control 39, no. 4 (2019): 1145–58. http://dx.doi.org/10.1177/1461348419847010.

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In order to realize the rapid response control for turboshaft engine during the process of variable rotor speed, the linear quadratic Gaussian with loop transfer recovery (LQG/LTR) control method for turboshaft engine based on torsional vibration suppression is proposed. Firstly, the two-speed dual clutch transmission model is applied to realize the variable rotor speed of helicopter. Then, based on the state variable model of turboshaft engine, the proper LQG/LTR controller is available. In order to eliminate the limitation of low-order torsional vibration on the bandwidth of LQG/LTR controller, a frequency-domain analysis method for the effect of torsional vibration suppression on LQG/LTR controller performance is developed. Finally, the numerical simulation is conducted to verify the LQG/LTR control for turboshaft engine with variable rotor speed based on torsional vibration suppression. The results show that the bandwidth of the LQG/LTR control loop can increase by 2–3 times under torsional vibration suppression. Meanwhile, when the rotor speed varies continuously by 40%, the overshoot and sag of the power turbine speed can decrease to less than 2% through LQG/LTR controller based on torsional vibration suppression, which achieves the rapid response control of the turboshaft engine.

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Li, Haisheng, Rongxuan Li, and Feng Wu. "A New Control Performance Evaluation Based on LQG Benchmark for the Heating Furnace Temperature Control System." Processes 8, no. 11 (2020): 1428. http://dx.doi.org/10.3390/pr8111428.

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Temperature control systems are a series of processes with large time-delay and non-linear characteristics. Research shows that using fractional-order modeling and corresponding control strategies can better control these processes. At the same time, the existing studies for control performance assessment are almost committed to the integer order control systems, and the methods used in few literatures on performance assessment of fractional order systems are also one-sided. This paper applies the linear quadratic Gaussian (LQG) evaluation benchmark to the performance evaluation of fractional-order control systems for the first time, starting with the LQG evaluation benchmark considering the input and output performance. The LQG benchmark can be obtained by the analytical algorithm, which simplifies the complexity of LQG solution. Finally, taking the application of the fractional predictive function control (FO-PFC) controller in the experiment of industrial heating furnace temperature control as an example, the effectiveness of the LQG benchmark is verified.

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UYGUR, Ali Fazıl. "CONTROL OF THE INVERTED PENDULUM ON A CART WITH THE MOPSO-BASED LQG SERVO CONTROL APPROACH." Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi 25, no. 3 (2022): 418–33. http://dx.doi.org/10.17780/ksujes.1133786.

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The Inverted Pendulum, located on the vehicle, is widely used in the academic sense for the application of various control methods and comparison of their performance. An unstable and non-linear Inverted Pendulum is sensitive and fragile to system disturbances and measurement noises. Exposure to disturbances and sensor measurement noise adversely affects the performance of control systems and causes a decrease in control quality. One of the methods used as a solution to the mentioned problem is the control design method known as Linear Quadratic Gaussian (LQG), which is a combination of Kalman Filter and LQR control.  In this study, for the Inverted Pendulum located on the vehicle, which is exposed to system disturbances and sensor noise, while the vehicle carrying the pendulum follows a given reference, the pendulum is required to maintain its unstable vertical equilibrium position. System disturbances and sensor noises are chosen as Gaussian White Noise. LQG servo control approach is adopted to provide reference tracking and maintain stability around the balance point. It is necessary to optimize the performance of the controller to be used in order to best meet the control requirements. For this purpose, the performance criterion weight matrices for the LQI block within the LQG servo controller have been optimized with the help of the Multi-Objective Particle Swarm Optimization Algorithm (MOPSO).

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Journal articles on the topic 'Linear-Quadratic-Gaussian (LQG) controller'

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