• Journal of Institute of Control, Robotics and Systems
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• v.20 no.11
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• pp.1085-1091
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• 2014

This paper presents the guidance and control design for automatic carrier landing of a UAV (Unmanned Aerial Vehicle). Differently from automatic landing on a runway on the ground, the motion of a carrier deck is not fixed and affected by external factors such as ship movement and sea state. For this reason, robust guidance/control law is required for safe shipboard landing by taking the relative geometry between the UAV and the carrier deck into account. In this work, linear quadratic optimal controller and longitudinal/lateral trajectory tracking guidance algorithm are developed based on a linear UAV model. The feasibility of the proposed control scheme and guidance law for the carrier landing are verified via numerical simulations using X-Plane and Matlab/simulink.

• Journal of Ocean Engineering and Technology
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• v.22 no.4
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• pp.14-19
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• 2008

This paper proposes a mixed $H_2/H_{\infty}$ optimal PID controller with a genetic algorithm based on the dynamic model of a brushless direct current (BLDC) motor and applies it to speed control. In the dynamic model of the BLDC motor with perturbation, the proposed controller guarantees arobust and optimal tracking performance to the desired speed of the BLDC motor. A genetic algorithm was used to obtain parameters for the PID controller that satisfy the mixed $H_2/H_{\infty}$ constraint. To implement the proposed controller, a control system based on PIC18F4431 was developed. Numerical and experimental results are shown to prove that the performance of the proposed controller was better than that of the optimal PID controller.

• 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.

• International Journal of Aeronautical and Space Sciences
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• v.7 no.2
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• pp.121-127
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• 2006

An analysis procedure for the combined problem of control algorithm and aeroelastic system which is based on the computational fluid dynamics(CFD) technique has been developed. The aerodynamic forces in the transonic region are calculated from the transonic small disturbance(TSD) theory. An linear quadratic regulator(LQR) controller is designed to suppress the transonic flutter. The optimal control gain is estimated by solving the discrete-time Riccati equation. The system identification technique rebuilds the CFD-based aeroelstic system in order to form an adequate system matrix which involved in the discrete-time Riccati equation. Finally the controller, that is constructed on the basis of system identification technique, is used to suppress the flutter phenomenon of the airfoil with attached store. This approach, that is, the CFD-based aeroservoelasticity design, can be utilized for the development of effective flutter controller design in the transonic region.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.266-268
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• 2006

In this paper, we design a Linear Quadratic Gaussian(LQG) controller for active suspensions. We can improve the inherent suspension problem, trade-off between the ride quality and the suspension travel by selecting appropriate weights in the LQ-objective function. Using an optimization-algorithm, Evolution Strategy(ES), we find the proper control gains for selected frequencies, which have major effects on the vibrations of the vehicle's state variables. The frequencies and proper control gains are used for the neural network data. During a vehicle running, the trained on-line neural network is activated and provides the proper gains for non-trained frequencies.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1530-1533
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• 2005

In this paper, we design a Linear Quadratic Gaussian controller for the active suspension. We can improve the inherent suspension problem, trade-off between the ride quality and the suspension travel by selecting appropriate weights in the LQ-objective function. Because any definite rules for selecting weights do not exist, we use an optimization-algorithm, Evolution Strategy (ES) to find the proper control gains for selected frequencies, which have major effects on the vibrations of the vehicle's state variables. The frequencies and proper control gains are used for the neural network data. During a vehicle running, the trained on-line neural network is activated and provides the proper gains for non-trained frequencies. For the full-state feedback control, Kalman filter observes the full states and Fourier transform is used to detect the frequency of the road.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.227-230
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• 1998

In this paper, we propose the design method of fuzzy robust H$\infty$ controller for the uncertain nonlinear discete-time systems with time delay. First, we represent a nonlinear plant with a modified T-S(Takagi-Sugeno) fuzzy model. Then design method utilizing the concept of PDC (parallel distributed compensation) is employed. For the modified T-S fuzzy model with uncertainty and delay, the sufficient condition of the quadratic stabilization with an H$\infty$ norm bound is presented in terms of Lyapunov stability theory and fuzzy robust H$\infty$ controller design method is given by LMI(linear matrix inequality) approach. Also an illustrative example is given to demonstrate the result of the proposed method.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.6
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• pp.1399-1411
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• 1994

A model helicopter has more versatile flight capability than the fixed-wing aircraft and it can be used as an unmaned vehicle in hazardous area. A helicopter, similar to other aircrafts, is an unstable, multi-input multi-output nonlinear system exposed to strong disturbance. So it should be controlled by robust control theories that can be applied to multivariable systems. In this study, motion equations of hovering are established, linearized and transformed into a state equation form. Various parameters are measured and calculated in other to obtain the stability derivatives in the state equation. Hovering flight controller is designed using the digital LQG/LTR(Linear Quadratic Gaussian/Loop Transfer Recovery) control theory. The designed controller is tested by the nonlinear simulations and implemented on an IBM-PC/386. Experiments were carried out on a model helicopter attached to the 3-DOF gimbal. The designed controller showed satisfactory hovering capability to maintain the hovering for more than 40 seconds.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.12 no.1
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• pp.12-20
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• 2002

To control the noise of an enclosed sound field, we built a state space model using the acoustic modal parameter description. Using the state space model, we can investigate the controllability and observability, and find an appropriate position of control speaker and microphone to control sound field of the enclosed space. We implemented LQR(linear quadratic regulator) controller and reduced order observer to reduce the first acoustic mode. Experiments showed satisfactory results of 4∼10 dB reduction of magnitude of the first acoustic mode, and support the feasibility of the proposed scheme to lightly damped acoustic field.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.17 no.5
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• pp.88-93
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• 2003

This paper presents a controller design method of bilinear systems via iterative method. The iterative procedure with auxiliary sequences is defined in the process of constructing coupled linear time varying systems from bilinear systems. To design the feedback controller for bilinear systems with quadratic cost function, an optimization procedure is given by the representation closely related to the Riccati approach. In the simulation results, it is shown that the suggested method accomplishes the improved performance and good convergence.