• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.312-315
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• 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.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.8
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• pp.1195-1200
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• 2013

In this paper, we proposed an enhanced Max-Min neural network by auto-tuning of learning rate using fuzzy control method. For the reduction of training time required in the competition stage, the method was proposed that arbitrates dynamically the learning rate by applying the numbers of the accuracy and the inaccuracy to the input of the fuzzy control system. The experiments using real concrete crack images showed that the enhanced Max-Min neural network was effective in the recognition of direction of the extracted cracks.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.8
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• pp.638-651
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• 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
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• 2000.10a
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• pp.367-367
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• 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.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.6
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• pp.776-782
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• 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.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.1
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• pp.15-21
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• 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
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• v.21 no.1
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• pp.11-17
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• 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.

• Water for future
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• v.19 no.4
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• pp.339-346
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• 1986

A real-time single reservoir operation model using the Min-max Dynamic Programming for the flood control of Soyanggang Dam and Choongju Dam is developed. The objective function is to minimize the maximum release from each dam and the constraints are those from ther reservoir and channel characteristics. Control and utilization efficiencies are used to measure the performance of the reservoir operation method (ROM). In comparison with those of simulation models(such as the Rigid ROM, the Technical ROM and the Linear Decision Rule), the efficiencies of the optimization model are superior for all return periods.

• Journal of Power Electronics
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• v.19 no.4
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• pp.968-979
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• 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.

• Journal of the Korea Society of Computer and Information
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• v.19 no.9
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• pp.33-38
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• 2014

The FCM based hybrid RBF network is a heterogeneous learning network model that applies FCM algorithm between input and middle layer and applies Max_Min algorithm between middle layer and output. The Max-Min neural network uses winner nodes of the middle layer as input but shows inefficient learning in performance when the input vector consists of too many patterns. To overcome this problem, we propose a dynamic learning rate control based on fuzzy logic. The proposed method first classifies accurate/inaccurate class with respect to the difference between target value and output value with threshold and then fuzzy membership function and fuzzy decision logic is designed to control the learning rate dynamically. We apply this proposed RBF network to the character recognition problem and the efficacy of the proposed method is verified in the experiment.