• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.224-224
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• 2000

Biaffine Matrix Inequalities(BIs) are known to give more general and flexible frameworks in control designs than Linear Matrix Inequalities(LMIs). However, BMIs are nonconvex constraints and very difficult to solve. In this paper, BMI problems are solved using Evolution Strategy(ES). Numerous BMI problems are solved to verify performances of ES solver for BMI problems and compared with those of Genetic Algorithms and Branch-and-Cut algorithm.

• The Transactions of the Korean Institute of Power Electronics
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• v.7 no.4
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• pp.384-394
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• 2002

In this paper, a new control strategy is proposed to improve the quality of the welding products. The conventional nonlinear power control system of spot welders is linearized using nonlinear feedback linearization technique based on differential geometry theory. An evolution strategy(ES) geometry is used to find optimal gain of PI controllers. It tries to find out the optimal control parameters by imitating the natural evolution. Some Simulation and experimental results show that the proposed variable power control system using ES algorithm has better dynamic performances than the conventional one.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.12
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• pp.1661-1666
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• 2014

In this paper, a compact planar ultra wideband (UWB, 3.1~10.6GHz) multiple-input multiple-output (MIMO) antenna is proposed. This antenna consists of two monopole planar UWB antennas and T-shaped stub decoupling between two antennas. The T-shaped stub improve the isolation characteristic at the wide band. The evolution strategy(ES) algorithm is employed to optimized design. As a result, optimized antenna has a return loss less than -10dB and the isolation less than -15dB from 3.1GHz to 10.6GHz. During the optimization process, the antenna gain is enhanced at lower band and the envelope correlation coefficient(ECC) is lower than 0.003.

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.531-533
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• 1997

PI(proportional-integral) controller has been extensively used in the industrial field. But in practicle case, it is difficult to tune PI gains. Evolution Strategy(ES) is used as an effective search algorithm in optimization programs. In this paper we proposed a PI controller for Spot welder system using ES with varying search space. ES with varying search space which depends on fitness values at each generation is used to tune PI control parameters. Simulation results show the proposed algorithm has accurate and robust performance with effective search ability.

• Journal of Electrical Engineering and Technology
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• v.10 no.2
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• pp.653-658
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• 2015

This paper deals with an efficient optimization design method of a compact ultra wideband (UWB) filter which can improve the characteristics of the filter. The Evolution Strategy (ES) algorithm is adopted for the optimization and modified to suppress the ripple by inserting an additional step to the ES scheme. The algorithm has the ability to control the ripple of an insertion loss in a passband as a modified approach. During the modified ES, a structure of initial shape is changed a lot, while includes the stepped impedance (SI) and the composite right/left handed transmission line (CRLH-TL). And an optimized filter satisfies the UWB specifications on the stopband and passband with an acceptable insertion loss. The filter achieves a much developed shape, the size of $15{\times}14mm$, the 3dB bandwidth from 2.7 to 10.8GHz, the flat insertion-loss less than 1dB, the wide stopband with 12~20GHz, and an acceptable return loss.

• Journal of Electrical Engineering and Technology
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• v.1 no.4
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• pp.435-447
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• 2006

This paper presents an application of the parallel Adaptive Evolutionary Algorithm (AEA) to search an optimal solution of the service restoration in electric power distribution systems, which is a discrete optimization problem. The main objective of service restoration is, when a fault or overload occurs, to restore as much load as possible by transferring the de-energized load in the out of service area via network reconfiguration to the appropriate adjacent feeders at minimum operational cost without violating operating constraints. This problem has many constraints and it is very difficult to find the optimal solution because of its numerous local minima. In this investigation, a parallel AEA was developed for the service restoration of the distribution systems. In parallel AEA, a genetic algorithm (GA) and an evolution strategy (ES) in an adaptive manner are used in order to combine the merits of two different evolutionary algorithms: the global search capability of the GA and the local search capability of the ES. In the reproduction procedure, proportions of the population by GA and ES are adaptively modulated according to the fitness. After AEA operations, the best solutions of AEA processors are transferred to the neighboring processors. For parallel computing, a PC cluster system consisting of 8 PCs was developed. Each PC employs the 2 GHz Pentium IV CPU and is connected with others through switch based fast Ethernet. To show the validity of the proposed method, the developed algorithm has been tested with a practical distribution system in Korea. From the simulation results, the proposed method found the optimal service restoration strategy. The obtained results were the same as that of the explicit exhaustive search method. Also, it is found that the proposed algorithm is efficient and robust for service restoration of distribution systems in terms of solution quality, speedup, efficiency, and computation time.

• Proceedings of the KIEE Conference
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• 1995.11a
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• pp.9-11
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• 1995

In this paper, tile squirrel case induction motors required multi-objective function are designed. As the objective function of the optimization program, we select the linear combination of loss and mass of motors by using weighting factors. Optimization process is performed by using the evolution strategy (ES). ES is the algorithm that can find the global minimum. To verify validity of the proposed method, a sample design is tried.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.6-8
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• 1996

The multiobjective optimization is presented for the optimal design of induction motors. The aim of design is to find an optimized induction motor in terms of both the efficiency and the mass. The efficiency and the mass are linearly combined using the weighting factors. Optimization process is performed by using the improved (1+1) evolution strategy (ES). ES is the algorithm that can find the global minimum. To verify the validity of the proposed method. the method is applied to a sample design.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.177-180
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• 1996

Fuzzy control systems depend on a number of parameters such as the shape or magnitude of the fuzzy membership functions, etc. Conventional fuzzy reasoning method can not be easily applied to the multi-input multi-output(MIMO) system due to the large number of rules in the rule base. Recently Z. Cao et al have proposed a New Fuzzy Reasoning Method(NFRM) which turned out to be superior to Zadeh's FRM. We have extended the NFRM to handle the MIMO system. However, it is difficult to choose a proper relation matrix of the NFRM. Therefore, we have modified the evolution strategy(ES), which is one of the optimization algorithms, to do efficiently the tuning operation for the extended NFRM. Finally we applied the extended NFRM with the modified ES to tracking control of robot manipulator.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.3
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• pp.209-217
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• 2001

In this paper, we propose a linear quadratic regulator(LQR) controller design for the active suspension using evolution strategy(ES) and neural network. We can improve the inherent suspension problem, the trade-off between ride quality and suspension travel by selecting appropriate weight in the LQR-objective function. Since any definite rules for selecting weights do not exist, we replace the designers trial-and-error method with ES that is an optimization algorithm. Using the ES, we can find the proper control gains for selected frequencies, which have major effects on the vibrations of the vehicle. The relationship between the frequencies and proper control gains are generalized by use of the neural networks. When the vehicle is driven, the trained neural network is activated and provides the proper gains for operating frequencies. And we adopted double sky-hook control to protect car component when passing large bump. Effectiveness of our design has been shown compared to the conventional sky-hook controller through simulation studies.