• International Journal of Control, Automation, and Systems
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• 제6권3호
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• pp.364-377
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• 2008

Due to the difficulty in solving the Hamilton-Jacobi-Isaacs equation, the nonlinear optimal control approach is not very practical in general. To overcome this problem, Ezal et al. (2000) first solved a linear optimal control problem for the linearized model of a nonlinear system given in the strict-feedback form. Then, using the backstepping procedure, a nonlinear feedback controller was designed where the linear part is same as the linear feedback obtained from the linear optimal control design. However, their construction is based on the cancellation of the high order nonlinearity, which limits the application to the smooth ($C^{\infty}$) vector fields. In this paper, we develop an alternative method for backstepping procedure, so that the vector field can be just $C^1$, which allows this approach to be applicable to much larger class of nonlinear systems.

• 통합자연과학논문집
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• 제11권4호
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• pp.167-183
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• 2018

Electroencephalogram (EEG) recording provides a new way to support human-machine communication. It gives us an opportunity to analyze the neuro-dynamics of human cognition. Machine learning is a powerful for the EEG classification. In addition, machine learning can compensate for high variability of EEG when analyzing data in real time. However, the optimal EEG electrode location must be prioritized in order to extract the most relevant features from brain wave data. In this paper, we propose an intelligent system model for the extraction of EEG data by training the optimal electrode location of EEG in a specific problem. The proposed system is basically a fuzzy system and uses a neural network structurally. The fuzzy clustering method is used to determine the optimal number of fuzzy rules using the features extracted from the EEG data. The parameters and weight values found in the process of determining the number of rules determined here must be tuned for optimization in the learning process. Genetic algorithms are used to obtain optimized parameters. We present useful results by using optimal rule numbers and non - symmetric membership function using EEG data for four movements with the right arm through various experiments.

• 대한기계학회논문집A
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• 제26권8호
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• pp.1719-1726
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• 2002

Design variables and design parameters are rarely deterministic in practice. Robust optimal design takes into consideration of the uncertainties in the design variables and parameters. Robust optimization methodology with probability constraints requires a lot of system analyses fer calculating failure probability of each constraint. By introducing an envelope function to reduce the number of constraints, efficiency of robust optimization techniques can be considerably improved. Through four illustrative examples, it is shown that the number of system analyses is greatly decreased while little differences in the optimum results are observed.

• Journal of Electrical Engineering and Technology
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• 제10권2호
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• pp.487-495
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• 2015

This paper proposes a new robust decentralized power oscillation dampers (POD) design of doubly-fed induction generator (DFIG) wind turbine for damping of low frequency electromechanical oscillations in an interconnected power system. The POD structure is based on the practical $2^{nd}$-order lead/lag compensator with single input. Without exact mathematical model, the inverse output multiplicative perturbation is applied to represent system uncertainties such as system parameters variation, various loading conditions etc. The parameters optimization of decentralized PODs is carried out so that the stabilizing performance and robust stability margin against system uncertainties are guaranteed. The improved firefly algorithm is applied to tune the optimal POD parameters automatically. Simulation study in two-area four-machine interconnected system shows that the proposed robust POD is much superior to the conventional POD in terms of stabilizing effect and robustness.

• 대한기계학회논문집A
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• 제25권9호
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• pp.1483-1492
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• 2001

A robust optimization procedure is applied to determine the design of the laminated composite plates with buckling constraints. In order to investigate the variation effect to the whole performance of a structure, both design variables and system parameters are assumed as random variables about their nominal values. The robust optimization method has advantages that the mean value and the variation of the performance function are controlled simultaneously and the second order sensitivity information is not required. Considering the information of uncertainty, robust optima for the buckling load of the laminated composite plates with cut-out is obtained. The robustness of the structures is compared to that of the deterministic optimization using scaling factors.

• 대한기계학회논문집A
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• 제21권5호
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• pp.859-869
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• 1997

In this paper, the robust design and tolerancing of the stabilized mirror is performed to increase its stabilization performance under vehicle vibration. Based on the sensitivity analysis, the seven important control factors are first identified, and then the optimal as well as robust values in the sense of Taguchi method are obtained. Finally, the tolerances associated with each design variables are determined based on a successive sensitivity analysis of the simulated system response so that the deviation in the response from the target value meets the specification requirements. The proposed tolerancing method features that it is a robust but conservative design method and that the computational effort is much less than the Monte Carlo simulation method.

• 경영과학
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• 제24권2호
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• pp.73-80
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• 2007

Robust Design(RD) is a cost-effective methodology to determine the optimal settings of control factors that make a product performance insensitive to the influence of noise factors. To better facilitate the robust design optimization, a dual response surface approach, which models both the process mean and standard deviation as separate response surfaces, has been successfully accepted by researchers and practitioners. However, the construction of response surface approximations has been limited to problems with only a few variables, mainly due to an excessive number of experimental runs necessary to fit sufficiently accurate models. In this regard, an innovative response surface approach has been proposed to investigate robust design optimization problems with larger number of variables. Response surfaces for process mean and standard deviation are partitioned and estimated based on the multi-level approximation method, which may reduce the number of experimental runs necessary for fitting response surface models to a great extent. The applicability and usefulness of proposed approach have been demonstrated through an illustrative example.

• 한국기계가공학회지
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• 제18권10호
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• pp.34-41
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• 2019

Warpage and weld line are two major cosmetic defects in the injection molding process. These defects are very sensitive to uncontrollable parameters within the process. The optimization of the design variables can be treated with the use of robust designs. Therefore, in order to minimize the warpage and weld line, a special design method to diminish defects is required. In this study, a new robust design method using designer preference to achieve the optimal robust design conditions in the injection molding process is proposed. The effectiveness of the proposed method is shown with an example of the part of warpage and weld line.

• 전자공학회논문지SC
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• 제46권3호
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• pp.15-21
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• 2009

본 논문에서는 변수 불확실성을 가지는 이산시간 특이시스템의 강인 안정화 기법과 강인 보장비용 제어기법을 다룬다. 제안하는 제어기법은 제어기 존재조건에서 준정부호조건(semi-definite condition)이나 시스템 행렬의 분해 없이 볼록최적화(convex optimization)가 가능한 선형행렬부등식 접근방법을 이용하여 제안한다. 먼저, 강인 안정화 상태궤환 제어기는 폐루프 시스템의 정규성, 코잘 및 안정화를 만족하는 제어기의 존재조건과 설계방법을 선형행렬부등식으로 제시한다. 그리고 보장비용 함수의 상한치의 최소화를 보장하는 강인 보장비용 제어기 설계방법은 강인 안정화 제어기 설계를 기반으로 제안한다. 예제를 통하여 제안한 제어기 설계기법의 타당성을 확인한다

• 전기학회논문지P
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• 제52권2호
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• pp.74-80
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• 2003

In this paper, a robust two-degree-of-freedom(TDF) the speed control system using $H_{\infty}$ optimization method and real genetic algorithm is proposed for the robust stability and the robust performance in dc servo motor system. This control system composed of feedback and feedforward controller. The feedback(FB) controller with $H_{\infty}$ optimization method is designed for real genetic algorithm that is model matching problem using mixed sensitivity function. The feedforward(FF) controller with $H_{\infty}$optimization method is minimized the error between transfer function of the optimal model and the overall transfer function. The proposed robust two-degree-of-freedom speed control system is simulated to the dc servo motor. By the simulation, feedback controller can obtain the robust stability property and feedforward controller can obtain the robust performance property under modelling error. The performance of the dc servo motor is analyzed by the experiment setting. The validity of the proposed method is verified through being compared with pid(proportional integrated differential)control system design method for the dc servo motor.