• Journal of Advanced Marine Engineering and Technology
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• v.26 no.4
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• pp.415-424
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• 2002

In this paper, a robust fuzzy precompensated PID controller using immune algorithm for load frequency control of 2-area power system is proposed. Here, a fuzzy precompensated PID controller is designed as a fuzzy logic based precompensation approach for PID controller. This scheme is easily implemented by adding a fuzzy precompensator to an existing PID controller. We optimize the fuzzy precompensator with an immune algorithm for complementing the demerit such as the difficulty of the component selection of fuzzy controller, namely, scaling factor, membership function and fuzzy rules. Simulation results show that the proposed robust load frequency controller can achieve good performance even in the presence of generation rate constraints.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.339-342
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• 2004

The x-y table of the SFFS to move a printer head must be the system that has a high speed and accuracy. So we propose the SMCSPO algorithm on the timing belt system. The major contribution is the design of a robust observer for the state and the perturbation of the timing belt system, which is combined with a robust controller. The control performance of the proposed algorithm is compared with PD control by the experiments. The results of SMCSPO algorithm showed more accuracy and better performance than PD control. Therefore we may apply the algorithms to a high speed and accuracy control for SFFS.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.1
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• pp.5-8
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• 1996

This paper proposea a new adaptive algorithm to improve the performance fo a robust adaptive control system. This adaptive algorithm counteracts the effects of disturbances and makes the time derivative of Lyapunov function non-positive, $V{\leq}0$. As a result, the output error approaches zero as $t\;\rightarrow\;\infty$ not only in the presence of bounded disturbances, but also in the ideal case. The effectiveness of this proposed algorithm is demonstrated by the stability analysis and simulation results.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.145-154
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• 1999

An intelligent system embedded with multiple sources of knowledge may provide more robust intelligence with highly ill structured problems than the system with a single source of knowledge. This paper proposes th hybrid knowledge integration mechanism that yields the cooperated knowledge by integrating expert, user, and machine knowledge within the fuzzy logic-driven framework, and then refines it with a genetic algorithm (GA) to enhance the reasoning performance. The proposed knowledge integration mechanism is applied for the prediction of Korea stock price index (KOSPI). Empirical results show that the proposed mechanism can make an intelligent system with the more adaptable and robust intelligence.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.10
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• pp.1865-1874
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• 2011

This paper proposes a novel maximum power point tracking(MPPT) using a new fuzzy control(NFC) algorithm for robust in insolation variation. Maximum power point(MPP) of solar cell has to achieve for improving output efficiency because it is changed with insolation and temperature. Conventional MPPT controller such as constant voltage(CV), perturbation and observation(PO) and incremental conductance(IC) are researched. But these controller have the problem that is failure to MPP with environment changing. The proposed NFC controller is based the fuzzy control algorithm and able to robust control with environment changing. Also the proposed controller of PV system is modeled by PSIM and the response characteristics according to the parameter variation is compared and analyzed. The validity of this controller is proved through response results.

• Industrial Engineering and Management Systems
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• v.6 no.2
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• pp.119-124
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• 2007

The most difficult and time-intensive issue in the successful implementation of genetic algorithms is to find good parameter setting, one of the most popular subjects of current research in genetic algorithms. In this study, we present a new efficient experimental design method for parameter optimization in a genetic algorithm for general job shop scheduling problem using the Taguchi method. Four genetic parameters including the population size, the crossover rate, the mutation rate, and the stopping condition are treated as design factors. For the performance characteristic, makespan is adopted. The number of jobs, the number of operations required to be processed in each job, and the number of machines are considered as noise factors in generating various job shop environments. A robust design experiment with inner and outer orthogonal arrays is conducted by computer simulation, and the optimal parameter setting is presented which consists of a combination of the level of each design factor. The validity of the optimal parameter setting is investigated by comparing its SN ratios with those obtained by an experiment with full factorial designs.

• Journal of Electrical Engineering and Technology
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• v.12 no.4
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• pp.1348-1356
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• 2017

Integration of wind generators with the conventional power plants will raise operational challenges to the electric power utilities due to the uncertainty of wind availability. Thus, the Generation Scheduling (GS) among the online generating units has become crucial. This process can be formulated mathematically as an optimization problem. The GS problem of wind integrated power system is inherently complex because the formulation involves non-linear operational characteristics of generating units, system and operational constraints. As the robust tool is viable to address the chosen problem, the modern bio-inspired algorithm namely, Grey Wolf Optimization (GWO) algorithm is chosen as the main optimization tool. The intended algorithm is implemented on the standard test systems and the attained numerical results are compared with the earlier reports. The comparison clearly indicates the intended tool is robust and a promising alternative for solving GS problems.

• Journal of the Korea Institute of Military Science and Technology
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• v.2 no.1
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• pp.90-100
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• 1999

유전자 알고리즘은 전통적인 등반 알고리즘을 이용하여 구하기 어려웠던 최적화 문제를 해결하기 위한 강인한(Robust) 탐색 기법이다. 특히 목적함수가 (1)여러 개의 국부 최대치를 가지는 경우, (2)수학적으로 표현이 불가능하거나 어려운 경우, (3)목적함수에 교란 항(disturbance term)이 섞여 있을 경우도 우수한 탐색 능력을 갖는 것으로 알려져 있다. 본 논문에서는 유전자 알고리즘을 이용하여 나타나는 다양한 해집합을 형성하는 개체군을 군집성 분석(cluster analysis)을 이용하여 군집화하고, 각 군집에 부여된 군집 적합도에 따라서 최적해를 구함으로써 단순 유전자 알고리즘에 의한 최적화보다 훨씬 향상된 탐색 알고리즘을 제안하였다. 반응표면의 형태가 정형화한 테스트 함수의 형태로 나타난다고 가정한 경우에 대하여 몬테 칼로 시뮬레이션을 통하여 본 알고리즘을 적용하여 평가하고 분석하였다.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2301-2306
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• 2005

Even if there are lots of researches on localization using 2D range finder in static environment, very few researches have been reported for robust real-time localization of mobile robot in uncertain and dynamic environment. In this paper, we present a new localization method based on ICP(Iterative Closest Point) algorithm for navigation of mobile robot under dynamic or uncertain environment. The ICP method is widely used for geometric alignment of three-dimensional models when an initial estimate of the relative pose is known. We use the method to align global map with 2D scanned data from range finder. The proposed algorithm accelerates the processing time by uniformly sampling the line fitted data from world map of mobile robot. A data filtering method is also used for threshold of occluded data from the range finder sensor. The effectiveness of the proposed method has been demonstrated through computer simulation and experiment in an office environment.

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1321-1323
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• 1999

In this paper, a robust $H_{\infty}$ optimal design problem under a structure-specified PSS is investigated for power systems with parameter variation and disturbance uncertainties. Genetic algorithm is employed for optimization method of PSS parameters. It is shown that the proposed $H_{\infty}$ PSS tuned using genetic algorithm is more robust than conventional PSS.