Crossing weighted uncertainty scenarios assisted distribution-free metamodel-based robust simulation optimization

Parnianifard, Amir; Azfanizam, A. S.; Ariffin, Mohd Khairol Anuar Mohd; Ismail, Mohd Idris Shah

doi:10.1007/s00366-018-00690-0

Cited by 20 publications

(19 citation statements)

References 30 publications

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“…Here, to find the bootstrapped set of data, a model is resampled times ( = 1,2, … , ) In this study, as inspired by [68], [84], the particular augmented bootstrapping approach is used for costly simulation running. In such a case, assume the set of sample points is fixed and only old data to fit surrogate with enough replication is available and new simulation replicating is very expensive.…”

Section: Sensitivity Analysis Of Optimal Results Using Augmented Bootmentioning

confidence: 99%

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A New Simulation-based Robust Optimal Design of FOPID Controller for Five Bar Linkage Robot in a Cyber-physical System

Parnianifard¹,

Wuttisittikulkij²,

Zemouche³

et al. 2020

Preprint

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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 changeability of a target of 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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Section: Sensitivity Analysis Of Optimal Results Using Augmented Bootmentioning

confidence: 99%

“…Here, we follow [37], [67], [68] and inspire Taguchi's overview of robust design [69] for dealing with uncertainty as a source of variability in the model. However, we expand…”

Section: Uncertainty Managementmentioning

confidence: 99%

A New Simulation-based Robust Optimal Design of FOPID Controller for Five Bar Linkage Robot in a Cyber-physical System

Parnianifard¹,

Wuttisittikulkij²,

Zemouche³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Here, we follow [39,69,70] and inspire Taguchi's overview of robust design [71] for dealing with uncertainty as a source of variability in the model. However, we expand Taguchi's robust design terminology and apply its definition for environmental noise factors in such a CPS control system.…”

Section: Uncertainty Managementmentioning

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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“…The distribution‐free aspect of SBRSO with one‐layer surrogate has been detailed in Reference 56. Here, the application of the two‐layer surrogate‐based methodology 57 for uncertainty management has been developed in the stochastic simulation‐optimization of control systems.…”

Section: Surrogate‐based Robust Simulation‐optimization (Two‐layer Sumentioning

confidence: 99%

“…

D_{s} = \frac{y_{s} - {\overset{falsê}{y}}_{- s}}{\sqrt{\frac{1}{l} \sum_{s = 1}^{l} {((), y_{s} - {\overset{falsê}{y}}_{- s})}^{2}}} ((), s = 1, 2, \dots, l)

where y s and

{\overset{falsê}{y}}_{- s}

are the prediction for s th sample point using original Kriging surrogate and surrogate constructed with removing s th sample, respectively. Note that the surrogate is accepted when most of the standardized residuals are located in the acceptable range of [−3, 3], see References 56,70. Details of the statistical procedure required for leave‐one‐out cross‐validation are provided in Reference 70.…”

Section: Surrogate‐based Robust Simulation‐optimization (Two‐layer Sumentioning

confidence: 99%

Robust tuning and sensitivity analysis of stochastic integer and fractional‐order PID control systems: application of surrogate‐based robust simulation‐optimization

Parnianifard

Fakhfakh

Kotti

et al. 2020

Int J Numerical Modelling

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This paper aims to make a trade‐off between performance and robustness in stochastic control systems with probabilistic uncertainties. For this purpose, we develop a surrogate‐based robust simulation‐optimization approach for robust tuning and analyzing the sensitivity of stochastic controllers. Kriging surrogate is combined with robust design optimization to construct a robust simulation‐optimization model in the class of dual response surfaces. Randomness in simulation experiments due to uncertainty is analyzed through bootstrapping technique by computing confidence regions for the estimation of Pareto frontier. Results confirmed a proper trade‐off between the model's performance with the measure of expected Integral Squared Error (ISE) and robustness against uncertainty in the plant's physical parameters. Finally, the proposed method is evaluated in terms of accuracy, computational cost, and simplicity particularly in comparison with some common existed techniques in the tuning of the Proportional‐Integral‐Derivative (PID) and Fractional‐Order PID (FOPID) controllers.

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Crossing weighted uncertainty scenarios assisted distribution-free metamodel-based robust simulation optimization

Cited by 20 publications

References 30 publications

A New Simulation-based Robust Optimal Design of FOPID Controller for Five Bar Linkage Robot in a Cyber-physical System

A New Simulation-based Robust Optimal Design of FOPID Controller for Five Bar Linkage Robot in a Cyber-physical System

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

Robust tuning and sensitivity analysis of stochastic integer and fractional‐order PID control systems: application of surrogate‐based robust simulation‐optimization

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