• International Journal of Aeronautical and Space Sciences
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• v.2 no.2
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• pp.73-81
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• 2001

This paper describes the procedure to develop a robust estimator design method for a target tracker that accounts for both structured real parameter uncertainties and unknown inputs. Two robust design approaches are combined: the Mini-p-Norm. design method to consider real parameter uncertainties and the $H_{\infty}$ design technique for unknown disturbances and unknown inputs. Constant estimator gains are computed that guarantee the robust performance of the estimator in the presence of parameter variations in the target model and unknown inputs to the target. The new estimator has two design parameters. One design parameter allows the trade off between small estimator error variance and low sensitivity to unknown parameter variations. Another design parameter allows the trade off between the robustness to real parameter variations and the robustness to unknown inputs. This robust estimator design method was applied to the longitudinal motion tracking problem of a T-38 aircraft.

• Journal of Korean Society for Quality Management
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• v.21 no.1
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• pp.96-107
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• 1993

In parameter design Taguchi analyzed a statistic which he called signal-to-noise(SN) ratio by using the experimental design technique. However he gave no justification for using SN ratios in the optimization procedure of parameter design. In this paper we discuss the validity of such SN ratios as proper statistics to be analyzed in parameter design. Moreover, using the real empirical data we examine the appropriateness of SN ratios, and we explain how transformation technique can be applied in parameter design as an alternative method of analysis.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2009.10a
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• pp.647-653
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• 2009

Taguchi parameter design is an approach to reducing performance variation of quality characteristic value in products and processes. Taguchi has used SN (Signal to Noise) ratio to achieve the appropriate set of operating conditions where variability around target is low in the Taguchi parameter design. This paper describes the prediction technique of vehicle performance using parameter analysis to reduce man hour and test development period as well as to achieve stable NVH performance. Design engineer could efficiently decide the design variable using parameter analysis database in early design stage. These improvements can reduce the time needed to develop better vehicles.

• Journal of Korean Institute of Industrial Engineers
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• v.38 no.3
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• pp.198-207
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• 2012

In product or process parameter design, the case of multiple performance characteristics appears more commonly than that of a single characteristic. Numerous methods have been developed to deal with such multi-characteristic parameter design (MCPD) problems. Among these, this paper considers three representative methods, which are respectively based on the desirability function (DF), grey relational analysis (GRA), and principal component analysis (PCA). These three methods are then used to solve the MCPD problems in ten case studies reported in the literature. The performance of each method is evaluated for various combinations of its algorithmic parameters and alternatives. Relative performances of the three methods are then compared in terms of the significance of a design parameter and the overall performance value corresponding to the compromise optimal design condition identified by each method. Although no method is significantly inferior to others for the data sets considered, the GRA-based and PCA-based methods perform slightly better than the DF-based method. Besides, for the PCA-based method, the compromise optimal design condition depends much on which alternative is adopted while, for the GRA-based method, it is almost independent of the algorithmic parameter, and therefore, the difficulty involved in selecting an appropriate algorithmic parameter value can be alleviated.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.68.5-68
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• 2001

This paper investigates the problem of linear parameter-dependent output feedback controllers design for interconnected linear parameter-varying(LPV) plant. By using a parameter-independent common Lyapunov function, sucient conditions for solving the problems are established, which allow us to design linear parameter dependent decentralized controllers in terms of scaled H-infinite control problems for related linear systems without interconnections. The solvability conditions are expressed in terms of finite-dimensional linear matrix inequalities(LMI´s) evaluated at the extreme points of the admissible parameter set.

• 한국데이터정보과학회:학술대회논문집
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• 2004.04a
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• pp.131-139
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• 2004

Robust parameter design is to identify appropriate settings of control factors that make the system's performance robust to changes in the noise factors that represent the source of variation. In this paper, we introduce a factor analysis approach to simultaneously optimize multiple quality characteristics in the robust parameter design. An example is illustrated to compare it with already proposed method.

• Journal of Korean Society for Quality Management
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• v.26 no.2
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• pp.17-26
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• 1998

Taguchi's parameter design is a method for quality improvement by making the performance fo a system robust to noise. Parameter design with dynamic characteristics has been recently the subject of much interest. This paper is concerned with a review and a generalization of the Signal-to-Noise (SN) ratio, a quality measure for parameter design with dynamic characteristics, proposed by Taguchi. We present a method for determination and analysis of the generalized SN ratio and illustrate its implementation by example.

• Journal of Integrative Natural Science
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• v.12 no.3
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• pp.69-73
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• 2019

Taguchi has used the signal-to-noise ratio (SN) to achieve the appropriate set of operating conditions where variability around target is low in the Taguchi parameter design. Taguchi has dealt with having constraints on both the mean and variability of a characteristic (the dual response problem) by combining information on both mean and variability into an SN. Many Statisticians criticize the Taguchi techniques of analysis, particularly those based on the SN. In this paper we propose a substantially simpler optimization procedure for parameter design to solve the dual response problems without resorting to SN.

• Journal of the Korea Safety Management & Science
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• v.13 no.1
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• pp.203-209
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• 2011

This study develops three new models that are practically applicable to three stages of Taguchi's robust design, which includes system design, parameter design and tolerance design. In system design, the Multiple Loss Function Analysis(MLFA) and Overall Loss Index(OLI) which reflect upon weight of characteristics and importance of specification are developed. Moreover parameter design presents Process Capability Index(PCI), $C_{PUK}$ and $C_{PLK}$, in order to segregate Signal-To-Noise Ratio(SNR) into accuracy and precision for an evaluation of relative comparison. In addition, tolerance design presents the new model of allowance computation for assembled product which is primarily derived from safety margin(SM) considering functional limit and specification. The guideline of those three new models, which include systematic charts and applicable illustrations, offers convenience for practitioners in the field. Hence, the practical applications could be made with the steps of robust designs such as system design, parameter design and specification allowance design.