• Journal of Institute of Control, Robotics and Systems
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• v.7 no.3
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• pp.209-217
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• 2001

In this paper, we propose a linear quadratic regulator(LQR) controller design for the active suspension using evolution strategy(ES) and neural network. We can improve the inherent suspension problem, the trade-off between ride quality and suspension travel by selecting appropriate weight in the LQR-objective function. Since any definite rules for selecting weights do not exist, we replace the designers trial-and-error method with ES that is an optimization algorithm. Using the ES, we can find the proper control gains for selected frequencies, which have major effects on the vibrations of the vehicle. The relationship between the frequencies and proper control gains are generalized by use of the neural networks. When the vehicle is driven, the trained neural network is activated and provides the proper gains for operating frequencies. And we adopted double sky-hook control to protect car component when passing large bump. Effectiveness of our design has been shown compared to the conventional sky-hook controller through simulation studies.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.1
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• pp.95-103
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• 2008

This paper deals with the problem of autolanding for aircraft under windshear environment for which the landing trajectory is given. It is well known that the landing maneuver in windshear turbulence is very dangerous and hard for the pilot to control because windshear is unpredictable in when and where it happens and its aerodynamic characteristics are complicated. In order to accomplish satisfactory autolanding maneuver in this environment, we propose a gain-scheduled controller. The proposed controller consists of three parts: PID controller, called baseline controller, which is designed to satisfy requirements of stability and performance without considering windshear, gain scheduler based on fuzzy logic, and safety decision logic, which decides if the current autolanding maneuver needs to be aborted or not. The controller is applied to a 6-DOF simulation model of the associated airplane in order to illustrate the effectiveness of the proposed control algorithm. It is noted that a cross wind in the lateral direction is included to the simulation model. From the simulation results it is observed that the proposed gain scheduled controller shows superior performance than the case of controller without gain scheduling even in severe downburst and tailwind region of windshear. In addition, touchdown along centerline of the runway is more precise for the proposed controller than for the controller without gain scheduling in the cross wind and the tailwind.

• Journal of Positioning, Navigation, and Timing
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• v.2 no.1
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• pp.67-74
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• 2013

In this study, a remote controller was designed using localization technique. The designed remote controller system consists of infrared transmit/receive module for time synchronization, ultrasonic transmit/receive module for measuring the TOA value, and micro-controller for processing the measured data value. For the position estimation method of remote controller, the Savarese method was used which does not have a problem of diverging solution depending on initial value. The noise included in the measured value was removed by separating the signal and noise with the use of EMD method which is the non-stationary signal analysis technique. The designed system was tested by constructing a simulation environment, and the improvement of accuracy and precision for the application of EMD method was examined.

• Journal of the Korea Convergence Society
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• v.4 no.2
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• pp.43-50
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• 2013

Sliding mode control(SMC) are carried out in this literature. And to make the controllers perform better, fuzzy logic was chosen,it makes PID controller auto-tuning parameters and reduced the chattering problem of sliding mode control. Since SMC take error and derivative of error as inputs, after comparison some results are obtained.PID controller response faster yet sliding mode control is much steadier. However certain problems cannot be ignored that the chattering phenomenal cannot be reduced entirely and this motion may hurt the machine; this project only considered a simple system, there is no guarantee PID can work as well as in this case for a much more complex system. MATLAB simulink was the main approach to obtain the performance of the two controllers: to observe the control output of the two controllers, electric circuit and special controllers are designed and tested in MATLAB.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.05a
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• pp.225-228
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• 2002

When the fuzzy logic controller (FLC), which is designed based on the plant model, is applied to the real control system, satisfactory control performance may not be attained due to modeling errors from the plant model. In such cases, the control parameters of the controller must be adjusted to enhance control performance. Until now, the trial and error method has been used, consuming much time and effort. To resolve such problem, response surface methodology (RSM), a new method of adjusting the control parameters of the controller, is suggested. This method is more systematic than the previous trial and error method, and thus optimal solutions can be provided with less tuning. First, the initial values of the control parameters were determined through the plant model and the optimization algorithm. Then, designed experiments were performed in the region around the initial values, determining the optimal values of the control parameters which satisfy both the rise time and overshoot simultaneously.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.414-416
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• 1998

One significant error in XY table is due to friction and disturbance. However, the characteristics of this friction is not easy to predict and analyze because of its nonlinearity. Therefore, it is difficult for conventional controller to compensate it effectively. In order to solve this problem, this paper presents a position controller based on fuzzy logic controller(FLC) that is suitable for system with unknown and unmodelled dynamics. The performance of the proposed controller are demonstrated by simulation results.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.735-738
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• 1997

The traffic congestion caused by the exploding increase of vehicles became one of the severest social problems. Among the various approaches to solve this problem, controlling the length of traffic signals appropriately according to the individual traffic situation would be the most plausible and cost-effective method. To design a traffic signal controller which has such a property as adaptive decision-making process, we adopt fuzzy logic control method(fuzzy traffic signal controller), Moreover, using genetic algorithms we obtain an optimized fuzzy traffic signal controller (GA-fuzzy traffic signal controller). To evaluate and validate the proposed fuzzy and GA-fuzzy traffic signal controller, simulation results are presented.

• Proceedings of the SAREK Conference
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• 2008.06a
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• pp.9-15
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• 2008

In this paper, we suggest PI control with a predictive controller to progress both energy saving and coefficient of performance(COP) in a variable speed refrigeration system. The capacity and superheat are controlled simultaneously and independently by an inverter and an electronic expansion valve respectively for saving energy and improving COP in the system. The refrigeration system has long dead time in superheat inherently. The dead time makes the system difficult to achieve the satisfactory quick control response, especially superheat control response. In order to solve this problem, we designed a predictive controller based on PI control to progress superheat control performance. The control performance was investigated through some experiments to verify the effectiveness of the predictive controller.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.58-65
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• 2003

In this paper, a new model, which is a Takagi-Sugeno fuzzy model, for mobile robot is presented. A controller, consisting of two loops the one of which is the inner state feedback loop designed for stability and the outer loop is a PI controller designed for tracking the reference input, is suggested. Because the robot dynamics is nonlinear, it requires the controller to be insensitive to the nonlinear term. To achieve this objective, the model is developed by well known T-S fuzzy model. The design algorithm of inner state-feedback loop is regional pole-placement. In this paper, regions, for which poles of the inner state feedback loop are lie in, are formulated by LMI's. By solving these LMI's, we can obtain the state feedback gains for T-S fuzzy system. And this paper shows that the PI controller is equivalent to the state feedback and the cost function for reference tracking is equivalent to the LQ(linear quadratic) cost. By using these properties, it is also shown in this paper that the PI controller can be obtained by solving the LQ problem.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.2
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• pp.102-108
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• 2001

In this paper, an active noise controller for a communication headset was designed. In a communication headset, there exist information signals such as voices from the end for the communication line as well as also, undesirable noises with are induced by external noise sources such as engine noises. Therefore, it is necessary to reduce the external noises for clear hearing of the communication signals. This problem was solved by robust H(sub)$\infty$ controller to reduce noise and a compensator for information signals The designed controller was implemented using TMS320C31 DSP Op-amp, and several experiments were performed to verify its performance. The results showed that the controller reduces the undesirable noises sufficiently, while communication signals are not reduced.