• 한국정보통신학회논문지
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• 제18권8호
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• pp.1811-1817
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• 2014

본 논문은 선도 로봇을 추종 로봇이 일정거리와 각도를 두고 추종하는 군집제어에서, 추종 로봇의 질량이 변할 경우, 신경회로망을 통해 보간된 이득을 갖는 PID제어기를 제안한다. 전체 제어시스템은 기구학 제어기와 동역학을 고려한 동적제어기로 구성하였다. 동적제어기는 가변 이득을 가지는 PID 제어기로 구성하여, 추종 로봇의 대표적 질량에 따라 적절한 PID 이득을 유전 알고리즘으로 구하였다. 유전 알고리즘으로 구한 데이터를 기초로 신경회로망을 학습하여 추종 로봇이 임의의 질량을 갖더라도 최적의 PID 이득을 선정할 수 있었다. 모의실험에서 추종 로봇의 질량이 임의의 값으로 변화하는 경우, 신경회로망을 통해 보간된 이득을 갖는 PID 제어기가 고정된 이득을 가지는 PID 제어기에 비해 군집제어에서 추종 성능을 향상시키는 것을 확인하였다.

• International Journal of Contents
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• 제3권3호
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• pp.20-25
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• 2007

Standards impact the economy in various ways. Moreover, intense competition exists between a variety of standards in this era of globalization. This paper quantifies the economic effect of multiple standards in the Information Communications and Technology (ICT) sector. Thus, it identifies and specifies which standard applies when economic gains exist. A model is developed which quantifies the magnitude of the economic effect of multiple standards as compared with a single standard or no standard. The model allows for both the micro- and macroeconomic gains from standardization to be quantified. Preliminary estimates indicate that at the macro level the multiple standards multiplier is approximately three. That is for every dollar invested, the gain is on the order of three dollars. Although not as robust. preliminary results indicated a similar economic gain at the micro level Overall, multiple standards dominate a single standard. This paper applies the model to IMT-2000, an example of multiple standards, to demonstrate this approach to quantify the standards economic effect.

• 제어로봇시스템학회논문지
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• 제10권12호
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• pp.1241-1248
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• 2004

A controller of wheeled mobile robot(WMR) based on Lyapunov theory is designed and a Fuzzy-Neural Network algorithm is applied to this system to adjust controller gain. In conventional controller of WMR that adopts fixed controller gain, controller can not pursuit trajectory perfectly when initial condition of system is changed. Moreover, acquisition of optimal value of controller gain due to variation of initial condition is not easy because it can be get through lots of try and error process. To solve such problem, a Fuzzy-Neural Network algorithm is proposed. The Fuzzy logic adjusts gains to act up to position error and position error rate. And, the Neural Network algorithm optimizes gains according to initial position and initial direction. Computer simulation shows that the proposed Fuzzy-Neural Network controller is effective.

• 제어로봇시스템학회논문지
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• 제7권3호
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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.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.295-298
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• 1996

In this paper, a self-tuning optimal control algorithm is proposed to retain the optimal performance of an active suspension system, when the vehicle has some time varying parameters and parameter uncertainties. We consider a 2 DOF time-varying quarter car model which has the parameter variation of sprung mass, suspension spring constant and suspension damping constant. Instead of solving algebraic riccati equation on line, we propose a neural network approach as an alternative. The optimal feedback gains obtained from the off line computation, according to parameter variations, are used as the neural network training data. When the active suspension system is on, the parameters are identified by the recursive least square method and the trained neural network controller designer finds the proper optimal feedback gains. The simulation results are represented and discussed.

• 대한전기학회논문지:시스템및제어부문D
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• 제50권1호
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• pp.22-30
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• 2001

This paper describes a neural network based fuzzy PID control scheme. The PID controller is being widely used in industrial applications. However, it is difficult to determine the appropriated PID gains in nonlinear systems and systems with long time delay and so on. In this paper, we re-analyzed the fuzzy controller as conventional PID controller structure, and proposed a neural network based self tuning fuzzy PID controller of which output gains were adjusted automatically. The tuning parameters of the proposed controller were determined on the basis of the conventional PID controller parameters tuning methods. Then they were adjusted by using proposed neural network learning algorithm. Proposed controller was simple in structure and computational burden was small so that on-line adaptation was easy to apply to. The experiment on the magnetic levitation system, which is known to be heavily nonlinear, showed the proposed controller's excellent performance.

• 한국지능시스템학회논문지
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• 제10권3호
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• pp.211-217
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• 2000

A main contribution of this paper is the development of a Hopfield network-based algorithm for the fault diagnosis of the actuators in linear system with uncertainties. An unknown input decoupling approach is introduced to the design of an adaptive observer so that the observer is insensitive to uncertainties. As a result, the output observation error equation does not depend on the effect of uncertainties. Simultaneous energy minimization by the Hopfield network is used to minimize the least mean square of errors of errors of estimates of output variables. The Hopfield network provides an estimate of the gains of the actuators. When the system dynamics changes, identified gains go through a transient period and this period is used to detect faults. The proposed scheme is demonstrated through its application to a simulated second-order system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 학술회의 논문집 정보 및 제어부문 B
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• pp.425-428
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• 2003

In this paper, the stabilization of an inverted pendulum system is studied. Here, the PID control method is adopted to make the system stable. In order to adjust the PID gains, a three-layer neural network, which is based on the back propagation method, is used. Meanwhile, the time for training the neural network depends on the initial values of PID gains and connection weights. Hence, the genetic algorithm Is considered to shorten the time to find the desired values. Simulation results show the effectiveness of the proposed approach.

• Journal of Communications and Networks
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• 제18권2호
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• pp.162-172
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• 2016

The placement of the complete baseband processing in a centralized pool results in high data rate requirement and inflexibility of the fronthaul network, which challenges the energy and cost effectiveness of the cloud radio access network (C-RAN). Recently, redesign of the C-RAN through functional split in the baseband processing chain has been proposed to overcome these challenges. This paper evaluates, by mathematical and simulation methods, different splits with respect to network level energy and cost efficiency having in the mind the expected quality of service. The proposed mathematical model quantifies the multiplexing gains and the trade-offs between centralization and decentralization concerning the cost of the pool, fronthaul network capacity and resource utilization. The event-based simulation captures the influence of the traffic load dynamics and traffic type variation on designing an efficient fronthaul network. Based on the obtained results, we derive a principle for fronthaul dimensioning based on the traffic profile. This principle allows for efficient radio access network with respect to multiplexing gains while achieving the expected users' quality of service.

• 천문학논총
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• 제37권1호
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• pp.1-11
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• 2022

During the course of analysing both single-dish and very long baseline interferometry (VLBI) data obtained from the Korean VLBI Network (KVN), we found a systematic offset between flux density measurements from different antennas. We were able to attribute a majority of the systematic offsets to changes in the "a priori" antenna gains, which were found to have varied up to 10 percent at 22 GHz and up to 30 percent at 43 GHz. Using historical calibrator observations, we present a revised set of gains that may be applied to KVN data taken from 2015 August to 2019 January. Application of the revised gains to the KVN results in a consistency of correlated flux density measurements between the three baselines of approximately five percent. We found that images from the recalibrated data typically have a 50 percent higher dynamic range, with some cases showing an increase of dynamic range of up to a factor of three.