Relevant bibliographies by topics / Linear-Quadratic-Gaussian (LQG) controller

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Linear-Quadratic-Gaussian (LQG) controller'

Author: Grafiati
Published: 3 June 2025
Last updated: 22 June 2025

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Journal articles on the topic "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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Dissertations / Theses on the topic "Linear-Quadratic-Gaussian (LQG) controller"

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Hadjikypris, Melios. "Supervisory control scheme for FACTS and HVDC based damping of inter-area power oscillations in hybrid AC-DC power systems." Thesis, University of Manchester, 2016. https://www.research.manchester.ac.uk/portal/en/theses/supervisory-control-scheme-for-facts-and-hvdc-based-damping-of-interarea-power-oscillations-in-hybrid-acdc-power-systems(cc03b44a-97f9-44ec-839f-5dcbcf2801f1).html.

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Modern interconnected power systems are becoming highly complex and sophisticated, while increasing energy penetrations through congested inter-tie lines causing the operating point approaching stability margins. This as a result, exposes the overall system to potential low frequency power oscillation phenomena following disturbances. This in turn can lead to cascading events and blackouts. Recent approaches to counteract this phenomenon are based on utilization of wide area monitoring systems (WAMS) and power electronics based devices, such as flexible AC transmission systems (FACTS) and HVDC links for advanced power oscillation damping provision. The rise of hybrid AC-DC power systems is therefore sought as a viable solution in overcoming this challenge and securing wide-area stability. If multiple FACTS devices and HVDC links are integrated in a scheme with no supervising control actions considered amongst them, the overall system response might not be optimal. Each device might attempt to individually damp power oscillations ignoring the control status of the rest. This introduces an increasing chance of destabilizing interactions taking place between them, leading to under-utilized performance, increased costs and system wide-area stability deterioration. This research investigates the development of a novel supervisory control scheme that optimally coordinates a parallel operation of multiple FACTS devices and an HVDC link distributed across a power system. The control system is based on Linear Quadratic Gaussian (LQG) modern optimal control theory. The proposed new control scheme provides coordinating control signals to WAMS based FACTS devices and HVDC link, to optimally and coherently counteract inter-area modes of low frequency power oscillations inherent in the system. The thesis makes a thorough review of the existing and well-established improved stability practises a power system benefits from through the implementation of a single FACTS device or HVDC link, and compares the case –and hence raises the issue–when all active components are integrated simultaneously and uncoordinatedly. System identification approaches are also in the core of this research, serving as means of reaching a linear state space model representative of the non-linear power system, which is a pre-requisite for LQG control design methodology.
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Book chapters on the topic "Linear-Quadratic-Gaussian (LQG) controller"

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Kumar, Gaurav, Wei Zhao, M. Noori, and Roshan Kumar. "Development of an Adaptive Linear Quadratic Gaussian (LQG) Controller for Structural Control Using Particle Swarm Optimization." In Data Driven Methods for Civil Structural Health Monitoring and Resilience. CRC Press, 2023. http://dx.doi.org/10.1201/9781003306924-8.

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Banek, Tadeusz, and Edward Kozlowski. "Active Learning in Discrete-Time Stochastic Systems." In Knowledge-Based Intelligent System Advancements. IGI Global, 2011. http://dx.doi.org/10.4018/978-1-61692-811-7.ch016.

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A general approach to self-learning based on the ideas of adaptive (dual) control is presented. This means that we consider the control problem for a stochastic system with uncertainty as a leading example. Some system’s parameters are unknown and modeled as random variables with known a’priori distribution function. To optimize an objective function, a controller has to learn the system’s parameter values. The main difficulty comes from the fact that he has to optimize the objective function parallely, i.e., at the same time. Moreover, these two goals considered separately not necessarily coincide and the main problem in the adaptive control is to find the trade-off between them. Looking from the self-learning perspective the two directions are visible. The first is to extract the learning procedure from an optimal adaptive control law and to formulate it as a Cybernetic Principle of self-learning. The second is to consider a control problem with the special objective function. This function has to measure our knowledge about unknown parameters. It can be the Fisher information (Banek & Kulikowski, 2003), the joint entropy (for example Saridis, 1988; Banek & Kozlowski, 2006), or something else. This objective function in the control problem will force a controller to steer a system along trajectories that are rich in information about unknown quantities. In this chapter the authors follow the both directions. First they obtain conditions of optimality for a general adaptive control problem and resulting algorithm for computing extremal controls. The results are then applied to the simple example of the Linear Quadratic Gaussian (LQG) problem. By using analytical results and numerical simulations the authors are able to show how control actions depend on the a’piori knowledge about a system. The first conclusion is that a natural, methodological candidate for the optimal self-learning strategy, the “certainty equivalence principle”, fails to satisfy optimality conditions. Optimal control obtained in the case of perfect system’s knowledge is not directly usable in the partial information case. The need of active learning is an essential factor. The differences between controls mentioned above are visible on a level of computations and should be interpreted on a higher level of cybernetic thinking in order to give a satisfactory explanation, perhaps in the form of another principle. Under absence of the perfect knowledge of parameters values, the control actions are restricted by some measurability requirement and the authors compute the Lagrange multiplier associated with this “information constraint”. The multiplier is called a “dual” or “shadow” price and in the literature of the subject is interpreted as an incremental value of information. The authors compute the Lagrange multiptier and analyze its evolution to see how its value changes as the time goes on. As a second sort of conclusion the authors get the self-learning characteristic coming from the information theory point of view. In the last section the authors follow the second direction. In order to estimate the speed of self-learning they choose as an objective function, the conditional entropy. They state the optimal control problem for minimizing the conditional entropy of the system under consideration. Using general results obtained at the beginning, they get the conditions of optimality and the resulting algorithm for computing the extremal controls. Optimal evolution of the conditional entropy tells much about intensivity of self-learning and its time distribution.
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Conference papers on the topic "Linear-Quadratic-Gaussian (LQG) controller"

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Ha, Chi, Albert DeWeese, Mark Wright, et al. "Automatic Fatigue Test Control System (AFTCS)." In Vertical Flight Society 73rd Annual Forum & Technology Display. The Vertical Flight Society, 2017. http://dx.doi.org/10.4050/f-0073-2017-12184.

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Bell Helicopter's next-generation Automatic Fatigue Test Control System (AFTCS) is presented. Fatigue testing places a helicopter part or specimen under repeated, controlled cyclic loading to determine if and when it will fail. A National Instruments PXIe embedded processor and LabVIEW software are used to control up to 48 linear/rotary hydraulic actuators in real-time. The actuators apply structural loads that are sensed by up to 256 strain gauges on the helicopter specimen. The structure and instrumentation respond to prescribed cyclic loads that range in frequency from 0.5 to 30 Hz, with a minimum resolution of 0.1 Hz. The fatigue test controller design is based on the well-known Linear-Quadratic-Gaussian control (LQG) methodology. Its feedback and filter gains are computed using a state-space model of the specimen identified separately. The controller computes any necessary changes in the actuator motions (at every time sample), maintaining acceptable margin between the measured and desired strain gauge responses. The system is demonstrated on a Bell Helicopter 429 tail rotor blade.
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Mounis, Shawgi Younis Ahmed, Norsinnira Zainul Azlan, and Sado Fatai. "Optimal Linear Quadratic Gaussian Torque Controller (LQG) for Upper Limb Rehabilitation." In 2019 7th International Conference on Mechatronics Engineering (ICOM). IEEE, 2019. http://dx.doi.org/10.1109/icom47790.2019.8952057.

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Moellenhoff, David E., S. Vittal Rao, and Charles A. Skarvan. "Design of Robust Controllers for Gas Turbine Engines." In ASME 1990 International Gas Turbine and Aeroengine Congress and Exposition. American Society of Mechanical Engineers, 1990. http://dx.doi.org/10.1115/90-gt-113.

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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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Jami'in, Mohammad Abu. "The Cascade Linear Quadratic Gaussian (LQG) Controller for Automatic Landing Systems in Aircraft." In 2020 International Conference on Applied Science and Technology (iCAST). IEEE, 2020. http://dx.doi.org/10.1109/icast51016.2020.9557592.

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Lopez, Luis Felipe, Joseph J. Beaman, and Rodney L. Williamson. "Linear-Quadratic-Gaussian (LQG) Controller for Liquid Pool Profile in Vacuum Arc Remelting." In ASME 2012 5th Annual Dynamic Systems and Control Conference joint with the JSME 2012 11th Motion and Vibration Conference. ASME, 2012. http://dx.doi.org/10.1115/dscc2012-movic2012-8546.

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Tomar, Basant, Narendra Kumar, and Mini Sreejeth. "Optimal Control of Rotary Inverted Pendulum Using Continuous Linear Quadratic Gaussian (LQG) Controller." In 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2023. http://dx.doi.org/10.1109/icccnt56998.2023.10306449.

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Shehata Gad, Ahmed. "Interval Lower Singleton Fuzzy Optimal Controller Design of Magnetorheological Seat Suspension Integrated with Semi-Active Vehicle Suspension System." In Automotive Technical Papers. SAE International, 2023. http://dx.doi.org/10.4271/2023-01-5066.

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<div class="section abstract"><div class="htmlview paragraph">In this paper, semi-active MR main suspension system based on system controller design to minimize pitch motion linked with MR-controlled seat suspension by considering driver’s biodynamics is investigated. According to a fixed footprint tire model, the transmitted tire force is determined. The linear-quadratic Gaussian (LQG) system controller is able to enhance ride comfort by adjusting damping forces based on an evaluation of body vibration from the dynamic responses. The controlled damping forces are tracked by the signum function controllers to evaluate the supply voltages for the front and rear MR dampers. Based on the sprung mass acceleration level and its derivative as the inputs, the optimal type-2 (T-2) fuzzy seat system controller is designed to regulate the controlled seat MR damper force. The best rate for each linguistic variable is acquired by modifying the range between upper and lower membership functions (MFs), which enables accurate tracking of the seat-damping force. The parameters of the LQG main system controller and the ideal scaling lower ranges of the T-2 fuzzy seat system controller are both explored by a genetic algorithm (GA). The performance of LQG regulated for MR dampers is compared with that of linear-quadratic regulator (LQR) controlled for MR dampers and passive systems to measure the suspension efficacy under bump and random road disturbance. To verify the efficiency of the recommended integrated models on both the main and seat systems, the performance of the proposed ideal T-2 fuzzy-controlled MR semi-active seat suspension is compared with the passive seat suspension. The simulation results show that the LQG controlled connected with the T-2 fuzzy controlled can greatly improve both ride comfort and vehicle stability, among all examined systems.</div></div>
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Rodríguez-Barrera, Jairo A., Jaime A. Parra-Raad, and Sebastián Roa-Prada. "Parameter Optimization of a Linear-Quadratic-Gaussian Controller for a Proton Exchange Membrane Fuel Cell Using Genetic Algorithms." In ASME 2014 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/imece2014-39183.

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Fuel cells are sources of clean energy which have become a key enabling technology in a wide spectrum of applications, ranging from automotive and aerospace applications to power supply for off-grid communities. The adequate functioning of a fuel cell requires permanent electrical power delivery to its load, operating at its maximum possible efficiency, even under load variations. Controlling the operating point of the fuel cell to manage changes in load conditions allows extending its service life. Several variables must be monitored and/or controlled to achieve optimal operating conditions of the fuel cell. This work deals with the design of a linear-quadratic-Gaussian, LQG, state-space controller for a proton exchange membrane fuel cell. The LQG controller is commonly used in fuel cell applications because it features an observer which can reconstruct states that are needed for the control strategy and that many times are difficult or too expensive to measure. The tuning of the parameters of the controller is performed by means of genetic algorithms procedures. The goal of the optimization is to prevent low levels of reactant gases due to sudden increases in the load. This will avoid damages to the membrane and other components of the stack while improving the overall performance of the system. The open loop and closed loop system response are presented using the lineal and non-lineal model of the plant. The response of the compensated system using the LQG controller is compared to the response using a basic state space controller, designed by the pole placing method, to assess the robustness of the LQG controller under disturbances. The results demonstrate the ability of the genetic algorithm technique to design a controller that can help preserving the integrity of the fuel cell while optimizing its performance.
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Priess, M. Cody, Jongeun Choi, and Clark Radcliffe. "The Inverse Problem of Continuous-Time Linear Quadratic Gaussian Control With Application to Biological Systems Analysis." In ASME 2014 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/dscc2014-6100.

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In this paper, we demonstrate two methods for solving the inverse problem of continuous-time LQG control. This problem can be defined as: given a known LTI system with feedback controller K and Kalman gain L, can we find the weighting matrices Q, R (for state and input, respectively) and estimated noise intensities W, V (for process and measurement noise, respectively) such that the LQG control synthesis problem using these weights generates K and L? We formulate a regularized version of this problem as a minimization problem subject to a set of Linear Matrix Inequalities (LMIs). If feasible, a unique exact solution to the inverse LQR problem exists. If the LMIs are infeasible, we show a gradient descent algorithm that will find Q, R, W, and V to minimize the error in the recovered gain matrices K and L. We demonstrate these techniques through several numerical examples and formulate a human postural control case study to which we intend to apply our proposed techniques.
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Zhu, Shenjin, and Yuping He. "Robust Controller Design for Active Suspension Systems of Road Vehicles." In ASME 2017 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/imece2017-70284.

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The Linear Quadratic Gaussian (LQG) technique has been applied to the design of active vehicle suspensions (AVSs) for improving ride quality and handling performance. LQG-based AVSs have achieved good performance if an accurate vehicle model is available. However, these AVSs exhibit poor robustness when the vehicle model is not accurate and vehicle operating conditions vary. The H∞ control theory, rooted in the LQG technique, specifically targets on robustness issues on models with parametric uncertainties and un-modelled dynamics. In this research, an AVS is designed using the H∞ loop-shaping control, design optimization, and parallel computing techniques. The resulting AVS is compared against the baseline design through numerical simulations.
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