• Transactions of the Korean Society of Mechanical Engineers B
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• v.39 no.6
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• pp.505-511
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• 2015

Solid oxide fuel cell operate at high temperature ($800{\sim}1000^{\circ}C$). High temperature have an advantage of system efficiency, but a weak durability. In this study, linear state space controller is designed to handle the temperature of solid oxide fuel cell system for proper thermal management. System model is developed under simulink environment with Thermolib$^{(R)}$. Since the thermally optimal system integration improves efficiency, very complicated thermal integration approach is selected for system integration. It shows that temperature response of fuel cell stack and catalytic burner are operated at severe non-linearity. To control non-linear temperature response of SOFC system, gain scheduled linear quadratic regulator is designed. Results shows that the temperature response of stack and catalytic burner follows the command over whole ranges of operations.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.5
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• pp.99-107
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• 2003

In a winding process, important control specifications include regulation of web tension and velocity. In this research, an adaptive controller has been developed for controlling web tension and velocity in winding processes. For the controller design, the linear quadratic regulator theory has been adopted and a gain-scheduling scheme has been incorporated. A prototype winding system has been constructed, and the controller has been implemented in a real-time PC-based environment. The performance of the closed loop system has been evaluated via simulation and experiments, and it was observed that both the web tension and velocity could be regulated within a small tolerance.

• International Journal of Control, Automation, and Systems
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• v.4 no.1
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• pp.1-9
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• 2006

This paper presents the modeling and multivariable feedback control of a novel high-precision multi-dimensional positioning stage. This integrated 6-degree-of-freedom. (DOF) motion stage is levitated by three aerostatic bearings and actuated by 3 three-phase synchronous permanent-magnet planar motors (SPMPMs). It can generate all 6-DOF motions with only a single moving part. With the DQ decomposition theory, this positioning stage is modeled as a multi-input multi-output (MIMO) electromechanical system with six inputs (currents) and six outputs (displacements). To achieve high-precision positioning capability, discrete-time integrator-augmented linear-quadratic-regulator (LQR) and reduced-order linearquadratic-Gaussian (LQG) control methodologies are applied. Digital multivariable controllers are designed and implemented on the positioning system, and experimental results are also presented in this paper to demonstrate the stage's dynamic performance.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.04a
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• pp.71-76
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• 2008

This paper is concerned with the active vibration control of elevator by means of the active roller guide. To this end, a dynamic model for the horizontal vibration of the elevator consisting of a supporting frame, cage and active roller guides was derived using the energy method. Free vibration analysis was then carried out based on the equations of motion. Active vibration controller was designed based on the equations of motion using the LQR theory and applied to the numerical model. Rail irregularity and wind pressure variation were considered as external disturbance in the numerical simulations. The numerical results show that the active vibration control of elevator is possible.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.17 no.5
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• pp.111-117
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• 2003

The Taylor model concept is introduced to design a controller with input and output data only. The parameters in Taylor model can be estimated using the input and output data and a controller can be designed based on Taylor model. The accuracy of Taylor model approximation can be improved by increasing the observation window and the order of Taylor model. The LQR method is applied to Taylor model to design power system stabilizers (PSS), and compared with the conventional PSS.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.311-316
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• 1994

In this study, a governing equation for 4-axis active magnetic bearing system composed of a rigid rotor and two radial magnetic bearings is derived. We find out that there are two kind of coupling between control axes in the system. And digital contralized controller is designed based on state-space approach and linear quadratic regulator(LQR) theory. By numerical simulation, it is shown what the designed controller can stabilize the system and control the coupling effectively using limited control input.

• Transactions of the Korean Society of Automotive Engineers
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• v.8 no.5
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• pp.165-172
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• 2000

The VDC(Vehicle Dynamic Control) is a control system whose target is to improve stability of a vehicle under lateral motion. A lateral vehicle motion, especially on a slippery road, can lead to a hazardous situation, and the situation can even worsen by the driver`s inappropriate response. In this paper, two VDC systems, a fuzzy-based controller and an LQR-based controller have been developed. The controllers take as input the yaw rate and the sideslip angle of either body or rear wheel, and they yield the direct yaw moment signal by which the vehicle can gain stability during cornering. Simulations have been conducted to evaluate the performance of the control system. The results indicated that the controllers can successfully improve vehicle stability under potentially dangerous driving conditions.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.7
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• pp.1309-1313
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• 2010

In this paper, we propose a novel guidance law for controlling an UAV(Unmanned Air-Vehicle) to follow a reference line in vertical plane. A kinematics model representing the relative motion of the UAV to the reference line is derived. And then LQR(Linear Quadratic Regulator) theory is applied to the model to derive the VLFG(Vertical Line Following Guidance) law. The resultant guidance law forms a gain-scheduling controller scheduled by a simple parameter $\sigma$ which is a function of the UAV's velocity, axial acceleration, gravity, and the slope of the reference line. Also derived is a stability condition for the $\sigma$ variation based on Lyapunov theory. Simulation results show that the proposed guidance law can be applied effectively to UAV guidance algorithm design.

• Journal of the Society of Naval Architects of Korea
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• v.49 no.1
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• pp.14-25
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• 2012

Recently, the application and installation of the pod propeller to the cruise ship is dramatically increased. It is because pod propulsion system allows a lot of flexibility in design of the internal arrangement of a ship. To reflect this trend, many researches have conducted to use the pod propeller for the roll stabilization of a ship. In the paper, a roll stabilization controller is designed by using fins and pod propellers as the control actuators for cruise ships. Two kinds of control algorithms are adopted for the roll control system; LQR (Linear Quadratic Regulator) algorithm and frequency-weighted LQR algorithm. Through the numerical simulation, the effect of the turning rate of the pod propeller on the roll control system is analyzed. Analysis of the simulation results indicated that the turning rate of the pod propellers is one of the important parameters which give the significant effects on the roll stabilization.

• Journal of the Korean Institute of Navigation
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• v.23 no.3
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• pp.35-44
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• 1999

This research aims to seek active control of ball-beam position stability by resorting to neural networks whose layers are given bias weights. The controller consists of an LQR (linear quadratic regulator) controller and a neural networks controller in parallel. The latter is used to improve the responses of the established LQR control system, especially when controlling the system with nonlinear factors or modelling errors. For the learning of this control system, the feedback-error learning algorithm is utilized here. While the neural networks controller learns repetitive trajectories on line, feedback errors are back-propagated through neural networks. Convergence is made when the neural networks controller reversely learns and controls the plant. The goals of teaming are to expand the working range of the adaptive control system and to bridge errors owing to nonlinearity by adjusting parameters against the external disturbances and change of the nonlinear plant. The motion equation of the ball-beam system is derived from Newton's law. As the system is strongly nonlinear, lots of researchers have depended on classical systems to control it. Its applications of position control are seen in planes, ships, automobiles and so on. However, the research based on artificial control is quite recent. The current paper compares and analyzes simulation results by way of the LQR controller and the neural network controller in order to prove the efficiency of the neural networks control algorithm against any nonlinear system.