• Journal of international Conference on Electrical Machines and Systems
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• v.3 no.2
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• pp.184-189
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• 2014

In this paper, a new design method of vibration suppression controller for multi-inertia (especially, 3-ineritia) resonance systems is proposed. The controller consists of a digital fuzzy controller for speed loop and a digital PI controller for current minor loop. The three scaling factor of the fuzzy controller and two PI controller gains are determined by Differential Evolution (DE). The DE is one of optimization techniques and a kind of evolutionary computation technique. In this paper, we have applied the DE/rand/1/bin strategy to design the optimal controller parameters. Comparing with the conventional design algorithm, the proposed method is able to shorten the time of the controller design to a large extent and to obtain accurate results. Finally, we confirmed the effectiveness of the proposal method by the computer simulations.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.11 no.2
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• pp.102-109
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• 1997

The width of fuzzy membership function and control rule has an effect on performance of the fuzzy controller for electric equipment systems. In this paper, the neuro-fuzzy controller is proposed to im¬prove the performance of fuzzy controller. It has the width of membership function, that is adapted to the electrical parameter using multi-layer neural network, it is applied to first order electric power system with dead time and various plant constant. The related simulation resolts show that the pro¬posed neuro fuzzy controller are superior characteristics of improved performance

• Journal of Power Electronics
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• v.12 no.5
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• pp.787-799
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• 2012

Real and reactive power flows on a transmission line interact inherently. This situation degrades power flow controller performance when independent real and reactive power flow regulation is required. In this study, a quasi multi-pulse interline power flow controller (IPFC), consisting of eight six-pulse voltage source converters (VSC) switched at the fundamental frequency is proposed to control real and reactive power flows dynamically on a transmission line in response to a sequence of set-point changes formed by unit-step reference values. It is shown that the proposed hybrid fuzzy-PI commanded IPFC shows better decoupling performance than the parameter optimized PI controllers with analytically calculated feed-forward gains for decoupling. Comparative simulation studies are carried out on a 4-machine 4-bus test power system through a number of case studies. While only the fuzzy inference of the proposed control scheme has been modeled in MATLAB, the power system, converter power circuit, control and calculation blocks have been simulated in PSCAD/EMTDC by interfacing these two packages on-line.

• Proceedings of the IEEK Conference
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• 2000.06e
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• pp.55-58
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• 2000

Previous works using a Multi-population Genetic Algorithm have divided chromosome into two components, rule sets and membership functions. However, in this case bad rule sets disturb optimization in good rule sets and membership functions. A new method for a Multi-population Genetic Algorithm suggests three components, good rule sets, bad rule sets, and membership functions. To show the effectiveness of this method, fuzzy controller is applied in a Truck Backing Problem. Results of the computer simulation show good adaptation of the proposed method for a Multi-population Genetic Algorithm.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.374-377
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• 2006

본 논문은 에어컨 시스템의 효율성과 안정성에 기초하여, 과열도와 저압을 제어하는 Fuzzy 제어기 설계를 제안한다. 에어컨 시스템은 Compressor(압축기), Condenser(응축기), Evaporator(증발기), Expansion Valve(확장 밸브) 로 구성되며, 각각의 기기에 대한 제어가 독립적으로 이루어져 있다. 기존의 제어가 한 제어기를 사용한 단일방식으로 이루어지다보니 에어컨 시스템의 특성인 냉매의 상태가 달라지면 시스템 전반적으로 그 영향이 파급되는 부분까지 고려를 해 주지 못하고, 제어기의 성능이 효율적이고 안정적이지 못했다. 본 논문에서는 에어컨 시스템의 효율과 안정도에 결정적인 영향을 미치는 과열도와 저압(증발기의 압력)을 제어하기 위해, 비선형성이 강하고 불확실하며 복잡한 시스템을 쉽게 제어할 수 있는 Fuzzy 제어기를 구성하여, Expansion Valve 와 Compressor 에서 동시에 제어하는 Multi 제어기를 설계한다. 제안된 Fuzzy 제어기는 이산형 lookup_table 방식과 연속형 간략추론 방식을 사용하여 제어기를 설계하고, 유전자 알고리즘(GAs)을 이용하여 최적의 Fuzzy 제어기의 환산계수를 구한다. 그리고 시뮬레이션 결과를 통해 이산형 lookup_table 방식과 연속형 간략추론 방식의 각각의 제어기를 사용한 결과를 비교한다.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.1
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• pp.7-12
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• 2007

In this paper, we introduce an approach to design multi-fuzzy controllers for the superheat and the low pressure that have an influence on energy efficiency and stabilization of aft conditioning system. Air conditioning system is composed of compressor, condenser several evaporators and several expansion valves. It is quite difficult to control the air conditioning system because the change of the refrigerant condition give an impact on the overall air conditioning system. In order to solve the drawback, we design multi-fuzzy controllers which control simultaneously both three expansion valve and one compressor for the superheat and the low pressure of air conditioning system. The proposed multi fuzzy controllers are given as two kinds of controller types such as a continuous simplified fuzzy inference type and a discrete fuzzy lookup_table type. Here the scaling factors of each fuzzy controller ate efficiently adjusted by veal coding type Genetic Algorithms. The values of performance index of the conventional type are compared with the simulation results of discrete lookup_table type and continuous simplified inference type.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.51 no.4
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• pp.163-168
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• 2002

This paper presents the comparison results of control algorithm for the simultaneous control of a multi output converter system that controls, simultaneously and independently, the separate Buck converter and Boost converter with the different specification by one DSP digital controller. As two separate converters are regulated by only one DSP, it is possible to achieve the simple digital control circuit for regulating the multi output DC-DC converter. By setting the software switch state, PI and Fuzzy controller can be applied as a controller for each converter without any change of hardware. Also, it is included the control characteristics comparison between PI and Fuzzy controller. The control characteristics of each PWM DC-DC converter is validated by experimental results.

• Journal of Advanced Marine Engineering and Technology
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• v.20 no.4
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• pp.11-19
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• 1996

Recently, fuzzy and neural network techniques have been successfully applied to control of complex and ill-defined system in a wide variety of areas, such as robot, water purification, automatic train operation system and automatic container crane operation system, etc. In this paper, we present a neuro-fuzzy controller which unifies both fuzzy logic and multi-layered feedforward neural networks. Fuzzy logic provides a means for converting linguistic control knowledge into control actions. On the other hand, feedforward neural networks provide salient features, such as learning and parallelism. In the proposed neuro-fuzzy controller, the parameters of membership functions in the antecedent part of fuzzy inference rules are identified by using the error backpropagation algorithm as a learning rule, while the coefficients of the linear combination of input variables in the consequent part are determined by using the least square estimation method. Finally, the effectiveness of the proposed controller is verified through computer simulation of an inverted pendulum system.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.192-194
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• 1996

This paper propose an optimal scaling gain tuning method of the fuzzy PI controller using Genetic Algorithm(GA). Scaling gains can reflect the control resolution and fuzziness of input/output variables. By the scaling gain method, the design of a fuzzy logic controller(FLC) can be simplified without affecting the system performance in comparison with multi-decision table method. In designing a fuzzy logic controller, the analytic approach method for the optimization is unavailable. Therefore GA is excellent optimization algorithms for scaling gain tuning. Using this optimal scaling gain tuning method, a good performance can be achieved both in transient and steady state.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.1
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• pp.24-29
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• 2005

In the fuzzy control for the multi-variable system, it is difficult to obtain the fuzzy rule. Therefore, the parallel structure of the independent single input-single output fuzzy controller using a pairing between the input and output variable is applied to the multi-variable system. However, among the input/output variables which arc not paired the interactive effects should be taken into account. these mutual coupling of variables affect the control performance. Therefore, for the control system with a strong coupling property, the control performance is sometimes lowered. In this paper, the effect of mutual coupling of variables is considered by the introduction of a neuro-fuzzy controller using relative gain matrix. This proposed neuro-fuzzy controller automatically adjusts the mutual coupling weight between variables using a neural network which is realized by back-propagation algorithm. The good performance of the proposed nero-fuzzy controller is verified through computer simulations on 200MW boiler systems.