Robust parameter design based on Gaussian process with model uncertainty

Feng, Zebiao; Wang, Jianjun; Ma, Yizhong; Tu, Yiliu

doi:10.1080/00207543.2020.1740344

Cited by 21 publications

(6 citation statements)

References 55 publications

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“…Feng et al 20 tackled the issue of building a response surface model based on input-output data obtained from a computer simulation, which might introduce some uncertainty in the optimal solutions due to the simplification of the real physical system. They employed a robust Gaussian process model to account for the uncertainty and used the Gibbs sampling method to formulate the final optimization problem that minimizes the quality loss function.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimization of degree of conformance for multi‐response systems considering model imprecision: A data‐driven metaheuristic approach

Hejazi

2023

Quality & Reliability Eng

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Quality‐based design optimization is a process consisting of factor identification, finding an empirical/physical model between responses and factors, and finding a factor setting leading to the best quality levels. Response surface methodology is a well‐grounded data‐driven approach that applies a collection of statistical‐mathematical techniques to achieve the design requirements. Today's complex systems are usually affected by nuisance factors and have more than one output characteristic with some degree of correlation. This study aims to develop two‐stage stochastic programming for a multi‐response optimization model based on the degree of conformance as the most comprehensive metric for such design problems. The proposed approach can include the model imprecision in the optimization procedure using the probabilistic properties of the estimated parameters. This method would be applied in highly sensitive processes or costly operations that require a more accurate setting to avoid extra reworks or scraps. A simulated annealing algorithm with a boosted scenario checking modification has been applied to cope with the expensive computations for problem instances with a large sample size of uncertain parameters. The proposed model has also analyzed two cases from the literature, and the results support its superiority compared to the existing approaches.

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Section: Literature Reviewmentioning

confidence: 99%

Optimization of degree of conformance for multi‐response systems considering model imprecision: A data‐driven metaheuristic approach

Hejazi

2023

Quality & Reliability Eng

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“…2,3 The purpose of RD is to select the opportune input settings to minimize the variability of an output response, while minimizing the bias between the response mean and the specific target. 4,5 The RBD is to make output responses to satisfy the reliability constraints by selecting the control variables. 6 Quality loss (QL) and failure risk cost (RC) can be used to evaluate the robustness and reliability of optimization results.…”

Section: Introductionmentioning

confidence: 99%

“…Robust design (RD) and reliability‐based design (RBD) are effective tools to realize continuous quality improvement and improve product competitiveness 2,3 . The purpose of RD is to select the opportune input settings to minimize the variability of an output response, while minimizing the bias between the response mean and the specific target 4,5 . The RBD is to make output responses to satisfy the reliability constraints by selecting the control variables 6 .…”

Section: Introductionmentioning

confidence: 99%

Life‐cycle reliability‐based robust design optimization for GP model with response uncertainty

Feng

Wang

et al. 2021

Quality & Reliability Eng

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Reliability-based robust design optimization (RBRDO) is a crucial tool for lifecycle quality improvement. Gaussian process (GP) model is an effective alternative modeling technique that is widely used in robust parameter design. However, there are few studies to deal with reliability-based design problems by using GP model. This article proposes a novel life-cycle RBRDO approach concerning response uncertainty under the framework of GP modeling technique. First, the hyperparameters of GP model are estimated by using the Gibbs sampling procedure. Second, the expected partial derivative expression is derived based on GP modeling technique. Moreover, a novel failure risk cost function is constructed to assess the life-cycle reliability. Then, the quality loss function and confidence interval are constructed by simulated outputs to evaluate the robustness of optimal settings and response uncertainty, respectively. Finally, an optimization model integrating failure risk cost function, quality loss function, and confidence interval analysis approach is constructed to find reasonable optimal input settings. Two case studies are given to illustrate the performance of the proposed approach. The results show that the proposed approach can make better trade-offs between the quality characteristics and reliability requirements by considering response uncertainty.

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“…After the CPS control system is designed and modeled by extensive simulation, tuning methods need to be expanded to address uncertainty and random disturbances in the system. In addition, ignoring the impact of uncertainty on the optimization model, the obtained optimal results may be far from the true optimum settings [ 32 ]. One of the main features in a reliable CPS design is the stability feature (robustness), which means no matter how the environment generates noise and uncertain factors, the control system should always reach a stable decision result eventually [ 33 ].…”

Section: Introductionmentioning

confidence: 99%

Robust optimal design of FOPID controller for five bar linkage robot in a Cyber-Physical System: A new simulation-optimization approach

et al. 2020

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This paper aims to further increase the reliability of optimal results by setting the simulation conditions to be as close as possible to the real or actual operation to create a Cyber-Physical System (CPS) view for the installation of the Fractional-Order PID (FOPID) controller. For this purpose, we consider two different sources of variability in such a CPS control model. The first source refers to the changeability of a target of the control model (multiple setpoints) because of environmental noise factors and the second source refers to an anomaly in sensors that is raised in a feedback loop. We develop a new approach to optimize two objective functions under uncertainty including signal energy control and response error control while obtaining the robustness among the source of variability with the lowest computational cost. A new hybrid surrogate-metaheuristic approach is developed using Particle Swarm Optimization (PSO) to update the Gaussian Process (GP) surrogate for a sequential improvement of the robust optimal result. The application of efficient global optimization is extended to estimate surrogate prediction error with less computational cost using a jackknife leave-one-out estimator. This paper examines the challenges of such a robust multi-objective optimization for FOPID control of a five-bar linkage robot manipulator. The results show the applicability and effectiveness of our proposed method in obtaining robustness and reliability in a CPS control system by tackling required computational efforts.

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Robust parameter design based on Gaussian process with model uncertainty

Cited by 21 publications

References 55 publications

Optimization of degree of conformance for multi‐response systems considering model imprecision: A data‐driven metaheuristic approach

Optimization of degree of conformance for multi‐response systems considering model imprecision: A data‐driven metaheuristic approach

Life‐cycle reliability‐based robust design optimization for GP model with response uncertainty

Robust optimal design of FOPID controller for five bar linkage robot in a Cyber-Physical System: A new simulation-optimization approach

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