• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 Proceedings of the Korea Automatic Control Conference, 11th (KACC); Pohang, Korea; 24-26 Oct. 1996
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• pp.358-361
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• 1996

In this paper, we discuss decentralized optimal fault tolerant supervisory control issues on the basis of failure analysis and diagnosis from the angle of discrete event dynamic system. We address the detectability and the observability problems, and develope fault tolerant supervisory control system upon the failure analysis and diagnosis schemes. A complete min-cut is introduced and the procedure for finding the achievable or nonachievable layered optimal legal sublanguages is suggested for a preferential option among the reachable states in the controlled plant. A layered optimal supervisory control framework is proposed upon these. We extend the concept of decentralized supervisory control by considering the problem of combination of decentralized with centralized control in case pure decentralized control happens to be inadequate. We introduce the concept of locally controllable pair and present a hybrid decentralized supervisory control framework. Finally, we propose the analytical framework for a decentralized optimal fault tolerant supervisory control systems.

• 한국생산제조학회지
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• 제26권4호
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• pp.402-409
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• 2017

Selection of optimal cutting conditions is important for improving productivity and implementing efficient process control in metal machining. In this study, improvement of cutting conditions in machining using end-mills is studied by using deep-layered neural networks, which comprise an input layer, output layer, and two hidden layers. System networks are designed with inputs as cutting conditions, and they output the cutting force. A pseudo-inverse network is designed that has the adjustable cutting condition as output and cutting force and other cutting conditions as input. The combination of the system network and pseudo-inverse network enables selection or improvement of cutting conditions that results in the expected cutting force.

• 한국전기전자재료학회논문지
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• 제27권12호
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• pp.866-870
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• 2014

In solid oxide fuel cell system, yttria-stabilized zirconia is generally adopted as the electrolyte, which has high strength and superior oxygen ion conductivity, and the air electrode and the fuel electrode are attached to this. Recently, new structure of 'layered planar SOFC module' was suggested to solve the reliability problem due to the high temperature stability of a sealing agent and a binding material. In this study to materialize the air electrode in a layered planar SOFC module, the LSM ink was coated to form homogeneous electrode in the channel after the ink preparation. As the porosity control agent, PMMA or active carbon powder was adopted with use of a commercial dispersant in ethanol. The optimal amounts of both the porosity control agents and the dispersant were determined. Four (4) vol% of the dispersant for the LSM-PMMA case and 15 vol% for LSM-carbon powder showed the lowest viscosities respectively to indicate the best dispersed states of the slurries. With PMMA and carbon powder, sintered LSM ink shows the relatively homogeneous distributions of pores and with increases of the agents, the porosities increased in both cases. From this, it can be thought that the amount of the PMMA or carbon powder could be used to control the porosity of the LSM ink.

• 전기학회논문지P
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• 제53권3호
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• pp.134-141
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• 2004

Recently, neural network techniques are widely used in adaptive and learning control schemes for production systems. However, in general it takes up a lot of time to learn in the case applied in control system. Furthermore, the physical meaning of neural networks constructed as a result is not obvious. And in practice since it is difficult for the PID gains suitably, lots of researches have been reported with respect of turning schemes of PID gains. A neural network-based PID control scheme is proposed, which extracts skills of human experts as PID gains. This controller is designed by using three-layered neural networks. The effectiveness of the proposed neural network-based PID control scheme is investigated through an application for a production control system. This control method can enable a plant to operate smoothy and obviously as the plant condition varies with any unexpected accidents.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 학술회의 논문집 정보 및 제어부문 B
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• pp.717-719
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• 2003

Recently Neural Network techniques have widely used in adaptive and learning control schemes for production systems. However, generally it costs a lot of time for learning in the case applied in control system. Furthermore, the physical meaning of neural networks constructed as a result is not obvious. And in practice since it is difficult to the PID gains suitably lots of researches have been reported with respect to turning schemes of PID gains. A Neural Network-based PID control scheme is proposed, which extracts skills of human experts as PID gains. This controller is designed by using three-layered neural networks. The effectiveness of the proposed Neural Network-based PID control scheme is investigated through an application for a production control system. This control method can enable a plant to operate smoothy and obviously as the plant condition varies with any unexpected accident.

• 제어로봇시스템학회논문지
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• 제5권1호
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• pp.95-104
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• 1999

In this paper we propose a fast and precise control algorithm for a mobile robot, which aims at the self-tuning control applying two multi-layered neural networks to the structure of computed torque method. Through this algorithm, the nonlinear terms of external disturbance caused by variable task environments and dynamic model errors are estimated and compensated in real time by a long term neural network which has long learning period to extract the non-linearity globally. A short term neural network which has short teaming period is also used for determining optimal gains of PID compensator in order to come over the high frequency disturbance which is not known a priori, as well as to maintain the stability. To justify the global effectiveness of this algorithm where each of the long term and short term neural networks has its own functions, simulations are peformed. This algorithm can also be utilized to come over the serious shortcoming of neural networks, i.e., inefficiency in real time.

• 로봇학회논문지
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• 제15권3호
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• pp.212-220
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• 2020

This paper deals with a strategy of gain optimization for the kinematic control algorithm of a wire-driven surgical robot. The proposed controller consists of the closed-loop inverse kinematics with the back-calculation method. The closed-loop inverse kinematics has 18 PID control gains, and the back-calculation method has 6 gains. An efficient strategy is designed to optimize 18 values first and then the remaining 6 values. The optimal gain sets are searched under the step input with performance indices. In this gain optimization, the objective function is defined as the minimum value of signal-to-noise ratio of the performance indices for 6 DoF (Degree-of-Freedom) motion that is based on the Taguchi method, and the constraints are applied to obtain stable responses for each motion evenly. The gain sets obtained are verified by simulations using the test trajectories. In comparative results, the optimal gain value based on the performance index combined with ISE (integral of square error) and settling time showed the best control performance.

• 제어로봇시스템학회지
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• 제6권4호
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• pp.38-48
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• 2000

Abstract :본 논문에서는 관리제어시스템의 동적특성을 허용언어(admissible language) 범위 이내에서 최적화시키는 최적 관리제어기법들을 소개한다. 본 논문에서 주로 다루고자 하는 최적 관리제어기법은 Kumar와 Garg에 의해 제안된 기법과 Cho와 Lim에 의해 제안된 계층적 최적 관리제어기법, 그리고, Sengupta와 Lafortune이 제안한 최적 관리제어기법 등이다. 첫 번째 기법에서는 우선 시스템의 최적화를 위해 고려되고 있는 비용함수(cost function)를 소개한 후, 최대흐름 최소분할생성 정리(max-flow min-cut theorem)를 이용한 최적 관리제어기 설계기법을 제시하고, 이를 부분관측 하에서도 최적 관리제어기를 설계할 수 있도록 확장한다. 그런 후 제시된 설계기법에 의해 설계된 관리제어시스템에서 발생 할 수 있는 문제점들을 지적하고, Cho와 Lim에 의해 제안된 완전 최소분할생성(complete min-cut)이라는 개념을 도입하여 지적된 문제점들을 해결할 수 있는 방법을 제시한다. 또한 시스템의 고장을 고려한 계층적 최적 관리 제어(layered optimal supervisory control)기 법을 소개한다 그리 고 마지막으로 Sengupta와 Lafortune이 제안한 최적 관리제어기법에 대해서 살펴본다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1996년도 추계학술대회 학술발표 논문집
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• pp.243-248
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• 1996

In this paper, a general neural-network-based connectionist model, called Fuzzy Neural Network(FNN), is proposed for the realization of a fuzzy logic control system. The proposed FNN is a feedforward multi-layered network which integrates the basic elements and functions of a traditional fuzzy logic controller into a connectionist structure which has distributed learning abilities. Such FNN can be constructed from training examples by learning rule, and the connectionist structure can be trained to develop fuzzy logic rules and find optimal input/output membership functions. Computer simulation examples will be presented to illustrate the performance and applicability of the proposed FNN, and their associated learning algorithms.

• 대한전자공학회논문지SP
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• 제39권6호
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• pp.575-584
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• 2002

동영상 부호화를 위한 새로운 방법으로 DCT 기반의 임베디드 제로트리 부호화와 연속되는 프레임에서의 최적 비트 배분을 위한 기법을 제안한다. 국제 표준화된 기존 부호화 알고리즘의 구조를 충분히 이용하면서 부호화 효율을 개선시키기 위해, 움직임 검출 및 보상과 DCT 복합 영상 부호화 구조 기반의 임베디드 제로트리 부호화 기법을 적용한다. 이를 위해 먼저, DCT 변환 계수를 복호 영상에 미치는 중요도에 따라 트리 구조로 재배치한 후, 임베디드 제로트리 부호화 알고리즘을 수정해서 적용한다. 이때, 주어진 전체 비트율에 대해, 연속되는 각 프레임에 최적의 비트가 배분되도록 해서 최상의 복호 영상을 얻기 위한 최적 비트율 제어기법을 제안한다. 또한, 각 프레임의 비트율 및 부호화 오차를 균일하게 제어해서 각 프레임마다 균일한 품질의 영상이 얻어지도록 한다. 제안 알고리즘의 객관적인 성능을 평가하기 위해 다양한 테스트 영상에 대해, 모의실험 결과를 제시하고, 그 성능의 우수성을 입증한다.