• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2003년도 ICCAS
• /
• pp.312-315
• /
• 2003

This paper deals with robust control problems of linear systems with matched nonlinear uncertainties. In order to handle the uncertainties, a Lyapunov min-max control approach can usually be adopted. By the way, the min-max control input is required to be switched and provokes chattering phenomena which limit the practical implementation. The magnitude of switching control input which cause chattering is dependent on the size of uncertainties. In this paper, it is shown that the magnitude of the min-max control input can be made small using a well-known disturbance observer technique and only considers the disturbance observing errors. The chattering phenomena can be reduced as small as possible by selecting a high diturbance observer gain. The simulations show that the min-max control with a disturbance observer can reduce chattering phenomena much smaller and guarantee much better robust performance rather than the one without a disturbance observer.

• 한국전자통신학회논문지
• /
• 제8권8호
• /
• pp.1195-1200
• /
• 2013

본 논문에서는 퍼지 제어 기법을 적용하여 학습률을 자동으로 조정하는 개선된 Max-Min 신경망을 제안하였다. 개선된 Max-Min 신경망은 경쟁 단계에서 필요한 학습 시간을 줄이기 위하여, 정확성의 수와 부정확성의 수를 퍼지 제어 시스템의 입력으로 적용하여 학습률을 동적으로 조정하는 기법이다. 본 논문에서 제안된 방법을 실제 콘크리트 표면 균열 영상에서 추출한 균열의 방향성 패턴을 대상으로 인식 실험한 결과, 개선된 Max-Min 신경망이 효과적임을 확인할 수 있었다.

• 제어로봇시스템학회논문지
• /
• 제6권8호
• /
• pp.638-651
• /
• 2000

When the bandwidth resources in a packet-switched network are shared among sessions by MAX-MIN flow control each session is required to transmit its data into the network subject to the MAX-MIN fair rate which is solely determined by network loadings. This passive behavior of sessions if fact can cause seri-ous QoS(Quality of Service) degradation particularly for real-time multimedia sessions such as video since the rate allocated by the network can mismatch with what is demanded by each session for its QoS. In order to alleviate this problem we extend the concept of MAX-MIN fair bandwidth allocations as follows: Individual bandwidth demands are guaranteed if the network can accommodate them and only the residual network band-width is shared in the MAX-MIN fair sense. On the other hand if sum of the individual bandwidth demands exceeds the network capacity the shortage of the bandwidth is shared by all the sessions by reducing each bandwidth guarantee by the MAX-MIN fair division of the shortage. we present a novel flow control algorithm to achieve this extended MAX-MIN fairness and show that this algorithm can be implemented by the existing ATM ABR service protocol with minor changes. We not only analyze the steady state asymptotic stability and convergence rate of the algorithm by appealing to control theories but also verify its practical performance through simulations in a variety of network scenarios.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
• /
• pp.367-367
• /
• 2000

In this paper, an Adaptive neuro-fuzzy Inference system(ANFIS) using fuzzy min-max network(FMMN) is proposed. Fuzzy min-max network classifier that utilizes fuzzy sets as pattern classes is described. Each fuzzy set is an aggregation of fuzzy set hyperboxes. Here, the proposed method transforms the hyperboxes into gaussian membership functions, where the transformed membership functions are inserted for generating fuzzy rules of ANFIS. Finally, we applied the proposed method to the classification problem of iris data and obtained a better performance than previous works.

• 한국지능시스템학회논문지
• /
• 제14권6호
• /
• pp.776-782
• /
• 2004

Generally, Max-Min CRI (Compositional Rule of Inference ) method by Zadeh and Mamdani is used in the conventional fuzzy inference. However, owing to the problems of Max-Min CRI method, the inference often results in significant error regions specifying the difference between the desired outputs and the inferred outputs. In this paper, I propose a New Max-Min CRI method which can solve some problems of the conventional Max-Min CRI method. And then this method is simulated in a D.C.series motor, which is a bench marking system in control systems, and showed that the new method performs better than the other fuzzy inference methods.

• 제어로봇시스템학회논문지
• /
• 제10권1호
• /
• pp.15-21
• /
• 2004

This paper deals with asymptotic stabilization problems for linear systems with time-varying input disturbances. In order to eliminate the influence of a disturbance on the system, a disturbance observer is designed and the time-varying disturbance can be rejected using its estimated value. Since the disturbance observer is kind of low-pass filter, it has inevitably estimation errors. To eliminate the inflences on the performance due to these errors, the additional control is designed based on these estimation errors using a well-known min-max control method. It is shown that the asymptotic stability of the closed-loop system is guaranteed. In general, the min-max control method requires the switching of control inputs and the switching magnitude of the control input is determined by the disturbance estimation error bounds. As the error bounds can be made arbitrarily small by choosing the high gain for the disturbance observer, the control method suggested in this paper can reduce the chattering phenomena as small as possible. Therefore, it has superior performance to the existing ones.

• Management Science and Financial Engineering
• /
• 제21권1호
• /
• pp.11-17
• /
• 2015

Min-max stochastic optimization is an approach to address the distribution ambiguity of the underlying random variable. We present a unified approach to the problem which utilizes the theory of convex order on the random variables. First, we consider a general framework for the problem and give a condition under which the convex order can be utilized to transform the min-max optimization problem into a simple minimization problem. Then extremal distributions are presented for some interesting classes of distributions. Finally, applications to the single-period inventory control problems are given.

• 물과 미래
• /
• 제19권4호
• /
• pp.339-346
• /
• 1986

소양강댐 및 충\ulcorner댐의 실시간 홍수조절 모형을 개발하기 위한 방법으로 Min-Max Dynamic Programming에 의한 최적화 기법을 사용하였다. 최적화 모형의 목적 함수로서는 각 댐의 최대 방류량을 최소화하도록 하였으며, 각 저수지 및 하도의 특성에 따른 제약 조건을 고려하였다. 개발된 단일 저수지 운영 모형에 의한 홍수조절 효과를 평가하는 척도로서 조절율과 이용율을 사용하였다. Technical ROM, Rigid ROM 및 Linear Decision rule과 같은 simulation 모형에 의한 조절 효과와 비교한 결과 모든 빈도에 대하여 DP에 의한 최적화 방법이 더 좋은 것으로 나타났다.

• Journal of Power Electronics
• /
• 제19권4호
• /
• pp.968-979
• /
• 2019

Recently, there has been increased interest in the study of multiphase machines due to their higher fault-tolerant capability when compared to their conventional three-phase counterparts. For six-phase machines, stator windings configured with a single isolated neutral (1N) provide significantly more post-fault torque/power than two isolated neutrals (2N). Hence, this configuration is preferred in applications where post-fault performance is critical. It is well known that min-max injection has been commonly used for three-phase and multiphase machines in healthy condition to maximize the modulation limit. However, there is a lack of discussion on min-max injection for post-fault condition. Furthermore, the effects in terms of the common-mode voltage (CMV) in modulating signals has not been discussed. This paper investigates the effect of min-max injection in post fault-tolerant control on the voltage and speed limit of a symmetrical six-phase induction machine with single isolated neutral. It is shown that the min-max injection can minimize the amplitude of reference voltage, which maximizes the modulation index and post-fault speed of the machine. This in turn results in a higher post-fault power.

• 한국컴퓨터정보학회논문지
• /
• 제19권9호
• /
• pp.33-38
• /
• 2014

FCM 기반하이브리드 RBF 네트워크는 서로 다른 학습 구조가 결합된 혼합형 모델로서, 입력층과 중간층의 학습 구조는 FCM 알고리즘을 적용하고, 중간층과 출력층 사이의 학습 구조는 Max_Min 알고리즘을 적용한다. 입력층과 중간층의 학습시 입력 벡터와 중간층의 노드 중에서 중심과 입력 벡터간의 가장 가까운 노드를 승자 노드로 선택하여 출력층으로 전달한다. 그리고 중간층과 출력층 사이의 학습구조인 Max_Min 신경망은 중간층의 승자 뉴런이 입력벡터로 적용된다. 그러나 많은 패턴이 입력벡터로 제시될 경우에는 학습성능이 저하되는 단점이 있다. 따라서 본 논문에서는 중간층과 출력층의 학습 구조인 Max_Min 알고리즘의 학습 성능을 향상시키기 위해 퍼지 논리 시스템을 이용한 학습률 자동 조정 방법을 제안한다. 제안된 방법은 목표값과 출력값의 차이에 대한 절대값이 0.1보다 적거나 같으면 정확성으로 분류하고 크면 부정확성으로 분류한다. 정확성의 총 개수를 퍼지 제어 시스템에 적용하여 학습률을 동적으로 조정한다. 제안된 방법의 학습 및 인식 성능을 평가하기 위해 컨테이너에서 추출한 숫자, 영문 식별자를 인식 및 성능평가 실험에 적용한 결과, 제안된 방법이 문자 패턴 인식에 효과적임을 확인할 수 있었다.