• Journal of Mechanical Science and Technology
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• 제14권6호
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• pp.590-596
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• 2000

Cause of excessive vibration with twice the rotational speed of a two-pole generator rotor for the fossil power plants was investigated. The two-pole generator rotor, treated as a typically asymmetric rotor in vibration analysis, produces asynchronous vibration with twice the rotational speed, sub-harmonic critical speeds, and potentially unstable operating zones due to its own inertia and/or stiffness asymmetry. This paper introduces a practical balancing procedure, and presents the results of the investigation on sources of the excessive vibration based on the experimental vibration data of the asymmetric two-pole rotor in balancing.

• 한국인터넷방송통신학회논문지
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• 제10권4호
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• pp.157-162
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• 2010

Cart-pole 균형 문제는 유전자 알고리즘, 인공신경망, 강화학습 등을 이용한 제어 전략 분야의 표준 문제이다. 본 논문에서는 cart-pole 균형문제를 해결하기 위해 실시간 강화 학습을 이용한 접근 방법을 제안하였다. 본 논문의 목적은 cart-pole 균형 문제에서 OREL 학습 시스템의 학습 방법을 분석하는데 있다. 실험을 통해, 본 논문에서 제안한 OREL 학습 방법은 Q-학습보다 최적 값 함수에 더 빠르게 접근함을 알 수 있었다.

• 소음진동
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• 제9권4호
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• pp.835-840
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• 1999

Cause of excessive vibration signals with twice the rotational speed of a 2-pole generator rotor in balancing for fossil power plants was investigated. The 2-pole generator rotor is treated as a typically asymmetric rotor in vibration analysis, and produces asynchronous vibration with twice the rotational speed for its own inertia and stiffness asymmetry. This paper introduces practical balancing procedure and experimental vibration data of the asymmetric 2-pole rotor in balancing, and presents the results of investigation into sources of the excessive vibration signals.

• 대한전기학회논문지
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• 제40권2호
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• pp.217-223
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• 1991

Most common applications of neural networks to control problems are the automatic motor controls using the artificial perceptual function. These control mechanisms are similar to those of the intelligent and pattern recognition control of an adaptive method frequently performed by the animate nature. In this paper, the pole-balancing problem is selected as the control object and an actual cart-pole controller is implemented by a computer interfacing and demonstrated as motor control using the reinforcement learning rule. In the experiment, given a change of the main parameters of cart-pole dynamics, a comparison is made between the LQR scheme and neural network method. The neural network method exhibits a more effecftive control action in a real situation having a large uncertainty than the LQR scheme.

• 동력기계공학회지
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• 제11권1호
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• pp.16-19
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• 2007

The large generator rotor used in fossil power plant has the possibility of high 2X vibration due to asymmetric shaft stiffness. The generator rotor is machined into pole faces to reduce stiffness difference and then is tested through 2X vibration measurement when the balancing works are performed in the balancing shop. However, there are many cases of large difference values between 2X vibration in the balancing shop and 2X vibration in site. This paper presents a new method to estimate 2X vibration in site with more accuracy and applied for the retrofit of a fossil 400 MW class deteriorated generator. It shows that the new generator rotor is manufactured with a good 2X vibration characteristics and is operated in a low 2X vibration level although the generator rotor has high 2X vibration in the balancing shop.

• 한국정밀공학회지
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• 제14권7호
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• pp.59-66
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• 1997

A general procedure for a motion capture and mimic system has been delineated. Utilizing sensors operated in the magnetic fields, complicated and optimized movements are easily digitized to analyze and repreduce. The system consists of a motion capture module, a motion visualization module, a motion plan module, a motion mimic module, and a GUI module. Design concepts of the system are modular, open, and user friendly to ensure the overall system performance. Custom-built and/or off-the-shelf modules are ease- ly integrated into the system. With modifications, this procedure can be applied for complicated motion controls. This procedure is implemented on tracking a head and balancing a pole. A neural controller based on this control scheme dtilizing human motions can easily evolve from a small amount of learning data.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2015년도 전력전자학술대회 논문집
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• pp.177-178
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• 2015

This paper presents a neutral point deviation compensating control algorithm applied to a 3-level NPC converter. The neutral point deviation is analyzed with a focus on the current flowing out of or into the neutral point of the dc link. Based on the zero sequence components of the reference voltages, this paper analyzes the neutral point deviation and balancing control for 3-level NPC converter. An analytical method is proposed to calculate the injected zero sequence voltage for NP balancing based on average neutral current. This paper also proposes a control scheme compensating for the neutral point deviation under generalized unbalanced grid operating conditions. The positive and negative sequence components of the pole voltages and ac input currents are employed to accurately explain the behavior of 3-level NPC converter. Simulation and experimental results for a test set up of 30kW are shown to verify the validity of the proposed algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1999년도 제14차 학술회의논문집
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• pp.73-78
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• 1999

This paper presents an effective construction method of adaptive multiple control systems utilizing some knowledge upon the plants. The adaptive multiple control system operates plants un-der widely changing environmental conditions. The adaptive multiple control system is composed of a family of candidate controllers together with a supervisor. The system does not require any identification schemes of environmental conditions. Monitoring outputs of the plant, the supervisor switches from one candidate controller to another, The basic ideas of adaptation are as follows: (1)each candidate controller is prepared for each environmental condition in advance; (2)the supervise. applies a sequence of speculative controls to the plant with candidate controllers just after the start of control or just after the detection of a change in the environmental condition. Each candidate controller can keep the system stable during one-step period of the speculative control and the most appropriate candidate controller for the environmental condition to which the system is exposed can be selected before the last trial of speculative control step comes to an end. We proposed a construction method of adaptive multiple control system without any knowledge of plant dynamics and applied the method to a cart-pole balancing problem and a vehicle anti skid braking system. In real applications, as we can often easily obtain a piece of knowledge upon plant dynamics beforehand, we intend to extend the method such that multiple control systems can be efficiently designed using the knowledge. We apply the new idea to the cart-pole balancing problem with variable length of the pole. The simulation experiments lead us to the conclusion that the new attempt can reduce the manpower to design the candidate controllers for adaptive multiple control systems.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2000년도 추계학술대회 학술발표 논문집
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• pp.163-167
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• 2000

The eligibility is used to solve the credit-assignment problem which is one of important problems in reinforcement learning. Conventional eligibilities which are accumulating eligibility and replacing eligibility make ineffective use of rewards acquired in learning process. Because only an executed action in a visited state is learned by these eligibilities. Thus, we propose a new eligibility, called the weighted eligibility with which not only an executed action but also neighboring actions in a visited state are to be learned. The fuzzy Q-learning algorithm using proposed eligibility is applied to a cart-pole balancing problem, which shows improvement of learning speed.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.531-531
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• 2000

The challenge is to control unstable nonlinear dynamic systems using only sparse feedback from the environment concerning its performance. The design of such controllers can be achieved by evolving neural networks. An evolutionary approach to train neural networks in realtime is proposed. Evolutionary strategies adapt the weights of neural networks and the threshold values of neuron's synapses. The proposed method has been successfully implemented for pole balancing problem.