Everything about Robust parameter design
A robust parameter design, introduced by Genichi Taguchi, is an experimental design used to exploit the interaction between control and uncontrollable noise variables by robustification—finding the settings of the control factors that minimize response variation from uncontrollable factors. Control variables are variables of which the experimenter has full control. Noise variables lie on the other side of the spectrum. While these variables may be easily controlled in an experimental setting, outside of the experimental world they are very hard, if not impossible, to control. Robust parameter designs use a naming convention similar to that of FFDs. A 2(m1+m2)-(p1-p2) is a 2-level design where m1 is the number of control factors, m2 is the number of noise factors, p1 is the level of fractionation for control factors, and p2 is the level of fractionation for noise factors. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Robust parameter designA 5-minute orientation
- Skim a recent paper: Constrained robust adaptive control design for fixed wing uav under parameter uncertainties and external disturbancesInfluential work since 2022
- Pick up a classic book: Neural Information ProcessingThe most-reprinted book on the subject
- Read the landmark paper: Parameter space design of robust control systemsCited 248 times
- Go sideways: Fractional factorial designA closely related topic
- Go sideways: Aliasing (factorial experiments)A closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Parameter space design of robust control systems
Robust Parameter Design: A Review
Design Potential Method for Robust System Parameter Design
An interval approach to robust design with parameter uncertainty
Robust Parameter Design Using Generalized Linear Mixed Models
A robust parameter design for multi-response problems
Robust optimization framework for process parameter and tolerance design
Robust Parameter Design: A Response Surface Approach
🆕 Influential since 2022
Constrained robust adaptive control design for fixed wing uav under parameter uncertainties and external disturbances
Framework for robust parameter design and optimization of reliability characteristics
Multi-response robust parameter design based on Bayesian mixed effects model
Robust Parameter Design on Dual Stochastic Response Models With Constrained Bayesian Optimization
Modelling and robust controller design for an underactuated self-balancing robot with uncertain parameter estimation
📚 Books worth reading
Neural Information ProcessingTingwen Huang · 2012
Medical device design for six sigmaBasem El-Haik · 2008
Computational Methods in Earthquake EngineeringManolis Papadrakakis · 2013
Computational Methods in Stochastic DynamicsManolis Papadrakakis · 2011
The Jackknife and BootstrapJun Shao, Dongsheng Tu · 1995
Understanding object-oriented programming with JavaTimothy Budd · 1998
TCP/IP IllustratedW. Richard Stevens, Kevin R. Fall · 1993
Robust Control Design for Active Driver Assistance SystemsPéter Gáspár, Zoltán Szabó · 2016
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Fractional factorial design In statistics, a fractional factorial design is a way to conduct experiments with fewer experimental runs than a full factorial design. Instead of testing…
- Aliasing (factorial experiments) In the statistical theory of factorial experiments, aliasing is the property of fractional factorial designs that makes some effects "aliased" with each…
- Glossary of experimental design These terms are used in experimental research and relevant to the fields of statistics, experimental design, and estimation theory. Alias: When the estimate…
- Plackett–Burman design Plackett–Burman designs are experimental designs presented in 1946 by Robin L. Plackett and J. P. Burman while working in the British Ministry of Supply…
- Taguchi methods Taguchi methods (Japanese: タグチメソッド) are statistical methods, sometimes called robust design methods, developed by Genichi Taguchi to improve the quality…
- Factorial experiment In statistics, a factorial experiment (also known as full factorial experiment) investigates how multiple factors influence a specific outcome, called…
- Yates analysis In statistics, a Yates analysis is an approach to analyzing data obtained from a designed experiment where a factorial design has been used. Full- and…
- Restricted randomization In statistics, restricted randomization, also known as constrained randomization, occurs in the design of experiments and in particular in the context…