• Proceedings of the KSR Conference
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• 2008.11b
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• pp.1220-1226
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• 2008

A route control in railway is one of the very important system to operate a train. An efficient train route control assures to increase train operation performance with a same railway system. The erroneous route control can accompany serious accidents which occur train collision or derailment which provokes death. A Route control carries out exactly lest the accident should take place. An interlocking table is widely used for the exact route control. The table has the problem of its exactness proving because it has been established by experts. In this paper, We tried to formalize a route control using mathematical logic. A route consists of symbolized tracks, signals, switch and crossing. It represents as a set, respectively. We proposed route setting control logic, converted the elements to set logics and construct route logics with the set logics of the elements. Finally we proposed a model which presents a prototype routes and we proved the proposed logics using the proposed method.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.3
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• pp.268-274
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• 2013

Any new method for controlling a nonlinear system has widely been reported. An inverted pendulum system has typically been used as a target system for demonstrating its usefulness. In this paper, we propose an algorithm to control a flexible joint cart based inverted pendulum system. Two carts are connected with a spring and one is a driving cart and the other is no driving cart with a pole. We here present a system modeling and a good fuzzy logic based control algorithm. We also introduce LQR (Linar Quadratic Regulator) technique for reducing the number of control variables. By using this technique, the number of input variables for a fuzzy logic controller is become only two not six. So the computational complexity is largely reduced. Moreover, a two-input fuzzy logic controller has a control rule table with a skew-symmetric property. And it will lead the design of a single-input fuzzy logic controller. In order to demonstrate the usefulness of the proposed method and prove the superiority of the proposed method, some computer simulations are presented.

• Transactions on Control, Automation and Systems Engineering
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• v.3 no.4
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• pp.203-209
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• 2001

This paper develops a control method for some generic formation tasks of multiple mobile robots with inaccurate sensor information. Inaccurate sensor information means that all the robots have only local sensors that cannot accurately measure absolute distances and directions of objects. That is, all the sensors have limitation on the range, and uncertainty in the values. Therefore, more robust and reliable control logic is proposed and implemented. The logic is developed considering generic situations and increasing the number of robots participating in the formation. Petri nets are used for modeling and design of the control logic, which can visualize the control models and make it easy to check the states of each robot. Physically homogeneous mobile robots are designed and built to evaluate the developed logic. Each robot is equipped with eighteen infrared sensors and a UHF transceiver module. The experiment results are analyzed quantitatively by using the data of the relative distances and angles between the robots. And the trajectories of the robots during the formation are also evaluated. The developed control approach is demonstrated with experiments to be successful and efficient for the formation of autonomous mobile robots.

• Journal of the Korean housing association
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• v.22 no.3
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• pp.113-122
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• 2011

This study aimed to develop AI- (Artificial Intelligence) based thermal control logics and test their performance for identifying the optimal thermal control method in buildings. For this objective, a conventional Two-Position On/Off logic and two AI-based variable logics, which applied ANN (Artificial Neural Network) and ANFIS (Adaptive Neuro-Fuzzy Inference System), have developed. Performance of each logic was tested in a typical two-story residential building in U.S.A. using the computer simulation incorporating MATLAB and IBPT (International Building Physics Toolbox). In the analysis of the test results, AI-based control logic presented the advanced thermal comfort with stability compared to the conventional logic while they did not show significant energy saving effects. In conclusion, the predictive and adaptive AI-based control logics have a potential to maintain interior air temperature more comfortably, and the findings in this study could be a solid foundation for identifying the optimal thermal control method in buildings.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.67 no.3
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• pp.143-148
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• 2018

In this paper, the optimal fuzzy logic controller(FLC) for a hybrid renewable energy system(HRES) is proposed. Generally, hybrid renewable energy systems can consist of wind power, solar power, fuel cells and storage devices. The proposed FLC can effectively control the entire HRES by determining the output power of the fuel cell or the absorption power of the electrolyzer. In general, fuzzy logic controllers can be optimized by classical optimization algorithms such as genetic algorithms(GA) or particle swarm optimization(PSO). However, these FLC have a disadvantage in that their performance varies greatly depending on the control parameters of the optimization algorithms. Therefore, we propose a method to optimize the fuzzy logic controller using the teaching-learning based optimization(TLBO) algorithm which does not have the control parameters of the algorithm. The TLBO algorithm is an optimization algorithm that mimics the knowledge transfer mechanism in a class. To verify the performance of the proposed algorithm, we modeled the hybrid system using Matlab Tool and compare and analyze the performance with other classical optimization algorithms. The simulation results show that the proposed method shows better performance than the other methods.

• Journal of the Korean Society of Propulsion Engineers
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• v.25 no.1
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• pp.68-76
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• 2021

The series hybrid system targeted in this study uses a reciprocating engine, a generator, and a battery as a main power source for the unmanned aerial vehicle. The generator is directly connected to the drive shaft of the reciprocating engine, and the operating characteristics of the reciprocating engine-generator set were confirmed through ground integration tests. In this study, based on the test results, a control logic is proposed an efficient use of the reciprocating engine-generator power and battery power. Also, the power variations of the reciprocating engine-generator and battery according to the logic were verified through simulation. As a result, it was confirmed that the engine-generator power supplied the power required for propulsion along with the battery power by the proposed control logic.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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• pp.1950-1955
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• 1991

A fuzzy dynamic learning controller is proposed and applied to control of time delayed, non-linear and unstable chemical processes. The proposed fuzzy dynamic learning controller can self-adjust its fuzzy control rules using the external dynamic information from the process during on-line control and it can create th,, new fuzzy control rules autonomously using its learning capability from past control trends. The proposed controller shows better performance than the conventional fuzzy logic controller and the fuzzy self organizing controller.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2345-2348
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• 2003

This paper deals with a problem of automatic landing guidance and control system design. The auto-landing control system for the longitudinal motion is designed in the classical PID controller. The controller gains are properly adapted to variation of the performance using fuzzy logic as a gain scheduler for the PID gains. This control logic is applied to the problem of the automatic landing control system design. From the numerical simulation using the 6DOF nonlinear model of the associated airplane, it is shown that the auto-landing maneuver is successfully achieved from the start of the flight conditions: 1500 ft altitude, 250 ft/sec airspeed and zero flight path angle.

• Journal of Drive and Control
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• v.17 no.1
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• pp.44-50
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• 2020

For the requirement of accurate tracking control and the safety of physical human-robot interaction, torque control is basically desirable for humanoid robots. Because of the complexity of humanoid robot dynamics, the TDC (time-delay control) is practical because it does not require a dynamic model. However, there occurs a considerable error due to discontinuous non-linearities. To solve this problem, the TDC-FLC (fuzzy logic compensator) is applied to humanoid robots. The applied controller contains three factors: a TDE (time-delay estimation) factor, a desired error dynamic factor, and FLC to suppress the TDE error. The TDC-FLC is easy to execute because it does not require complicated humanoid dynamic calculations and the heuristic fuzzy control rules are intuitive. TDC-FLC is implemented on the whole body of a humanoid, not on biped legs even though it is performed by a virtual humanoid robot. The simulation results show the validity of the TDC-FLC for humanoid robots.

• 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.