Kriging-assisted robust black-box simulation optimization in direct speed control of DC motor under uncertainty

In spite of the wide improvement in computer simulation packages, analyzing, and optimizing the simulation model, particularly under uncertainty can still be computationally expensive and time-consuming. This paper aims to tackle these features by proposing a comprehensive methodology applied to black-box stochastic simulation models under uncertainty. For this purpose, the common surrogate model as Kriging metamodel is served to fit the simulation input-output data produced by Latin hypercube sampling experimental design. Taguchi terminology of robust design enables the optimization methods to control uncertainty and uncontrollable environmental factors. So as to formulate robust counterpart optimization, three different models in the class of dual-response surface are integrated with metamodel and robust design. Leave-one-out cross-validation is applied to validate the Kriging metamodel. Finally, a numerical case study as a direct speed control of dc motor under uncertainty is served to demonstrate the applicability of the proposed method in real engineering problems. This simplified and practical mechatronics case illustrates how the proposed procedure can be expanded for analyzing and optimizing the real complex systems

Digital-Twins towards Cyber-Physical Systems: A Brief Survey

Cyber-Physical Systems (CPS) are integrations of computation and physical processes. Physical processes are monitored and controlled by embedded computers and networks, which frequently have feedback loops where physical processes affect computations and vice versa. To ease the analysis of a system, the costly physical plants can be replaced by the high-fidelity virtual models that provide a framework for Digital-Twins (DT). This paper aims to briefly review the state-of-the-art and recent developments in DT and CPS. Three main components in CPS, including communication, control, and computation, are reviewed. Besides, the main tools and methodologies required for implementing practical DT are discussed by following the main applications of DT in the fourth industrial revolution through aspects of smart manufacturing, sixth wireless generation (6G), health, production, energy, and so on. Finally, the main limitations and ideas for future remarks are talked about followed by a short guideline for real-world application of DT towards CPS

Robust optimal design of FOPID controller for five bar linkage robot in a cyber-physical system: a new simulation-optimization approach

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

A data driven approach in less expensive robust transmitting coverage and power optimization

This paper aims the development of a new reduced-cost algorithm for a multi-objective robust transmitter placement under uncertainty. Toward this end, we propose a new hybrid Kriging/Grey Wolf Optimizer (GWO) approach combined with robust design optimization to estimate the set of Pareto frontier by searching robustness as well as accuracy (lower objective function) in a design space. We consider minimization of the energy power consumption for transmitting as well as maximization of signal coverage in a multi-objective robust optimization model. The reliability of the model to control signal overlap for multiple transmitting antennas is also provided. To smooth computational cost, the proposed method instead of evaluating all receiver test points in each optimization iteration approximates signal coverages using Kriging interpolation to obtain optimal transmitter positions. The results demonstrate the utility and the efficiency of the proposed method in rendering the robust optimal design and analyzing the sensitivity of the transmitter placement problem under practically less-expensive computational efforts (350% and 320% less than computational time elapsed using standalone GWO and NSGAII respectively)

Trade-off in robustness, cost and performance by a multi-objective robust production optimization method

Designing a production process normally is involved with some important constraints such as uncertainty, trade-off between production costs and quality, customer's expectations and production tolerances. In this paper, a novel multi-objective robust optimization model is introduced to investigate the best levels of design variables. The primary objective is to minimize the production cost while increasing robustness and performance. The response surface methodology is utilized as a common approximation model to fit the relationship between responses and design variables in the worst-case scenario of uncertainties. The target mean ratio is applied to ensure the quality of the process by providing the robustness for all types of quality characteristics and with a trade-off between variability and deviance from the ideal point. The Lp metric method is used to integrate all objectives in one overall function. In order to estimate target value of the quality loss by considering production tolerances, the process capability ratio () is applied. At the end, a numerical chemical mixture problem is served to show the applicability of the proposed method

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

In practice, computer simulations cannot be perfectly controlled because of the inherent uncertainty caused by variability in the environment (e.g., demand rate in the inventory management). Ignoring this source of variability may result in suboptimality or infeasibility of optimal solutions. This paper aims at proposing a new method for simulation–optimization when limited knowledge on the probability distribution of uncertain variables is available and also limited budget for computation is allowed. The proposed method uses the Taguchi robust terminology and the crossed array design when its statistical techniques are replaced by design and analysis of computer experiments and Kriging. This method ofers a new approach for weighting uncertainty scenarios for such a case when probability distributions of uncertain variables are unknown without available historical data. We apply a particular bootstrapping technique when the number of simulation runs is much less compared to the common bootstrapping techniques. In this case, bootstrapping is undertaken by employing original (i.e., non-bootstrapped) data, and thus, it does not result in a computationally expensive task. The applicability of the proposed method is illustrated through the Economic Order Quantity (EOQ) inventory problem, according to uncertainty in the demand rate and holding cost

An overview on robust design hybrid metamodeling: Advanced methodology in process optimization under uncertainty

Nowadays, process optimization has been an interest in engineering design for improving the performance and reducing cost. In practice, in addition to uncertainty or noise parameters, a comprehensive optimization model must be able to attend other circumstances which might be imposed in problems under real operational conditions such as dynamic objectives, multi-response, various probabilistic distribution, discrete and continuous data, physical constraints to design parameters, performance cost, computational complexity and multi-process environment. The main goal of this paper is to give a general overview of topics with brief systematic review and concise discussions about the recent development of comprehensive robust design optimization methods under hybrid aforesaid circumstances. Both optimization methods of mathematical programming based on Taguchi approach and robust optimization based on scenario sets are briefly described. Metamodels hybrid robust design is discussed as an appropriate methodology to decrease computational complexity in problems under uncertainty. In this context, the authors’ policy is to choose important topics for giving a systematic picture to those who wish to be more familiar with recent studies about robust design optimization hybrid metamodels, also by attending real circumstances in practice. In particular, production and project management are considered as two important methodologies that could be improved by applications of advanced robust design combining with metamodel methods

Recent developments in metamodel based robust black-box simulation optimization: an overview

In the real world of engineering problems, in order to reduce optimization costs in physical processes, running simulation experiments in the format of computer codes have been conducted. It is desired to improve the validity of simulation-optimization results by attending the source of variability in the model’s output(s). Uncertainty can increase complexity and computational costs in Designing and Analyzing of Computer Experiments (DACE). In this state-of the art review paper, a systematic qualitative and quantitative review is implemented among Metamodel Based Robust Simulation Optimization (MBRSO) for black-box and expensive simulation models under uncertainty. This context is focused on the management of uncertainty, particularly based on the Taguchi worldview on robust design and robust optimization methods in the class of dual response methodology when simulation optimization can be handled by surrogates. At the end, while both trends and gaps in the research field are highlighted, some suggestions for future research are directed

Kriging and Latin hypercube sampling assisted simulation optimization in optimal design of PID controller for speed control of DC motor

Investigating less computationally expensive methods for achieving optimal tuning of proportional, integral, and derivative gains in PID controller also has become a main challenging topic in the world of control systems. This paper aims to propose a new less-expensive method to the optimal design of PID controller. For this purpose, Kriging metamodel is used with Latin Hypercube Sampling (LHS) as a common experimental design method in the class of space filling design. Kriging can interpolate over whole design space and assisted to investigate global optimum point. A numerical case in the tuning of PID controller for linear speed control of DC motor is served to show the applicability and superiority of proposed method compared to two existing methods such as traditional Zeigler-Nichols method and Taguchi-Grey Relational Analysis (Taguchi-GRA)

Development of metamodel-based robust simulation optimization for complex systems under uncertainty

Computer simulations can help a rapid investigation of various alternative designs to decrease the required time to improve the system. Because of the complexity for analyzing complex systems in way of mathematical formulation, a simulation optimization has been an interest in analyzing and studying the behavior of complex systems in the real world of engineering problems. One of the main difficulties of existing model–based simulation optimization methods is dealing with large number of required simulation evaluation (also called simulation experiments or computer experiments) which causes of costly computational time. In addition, in order to improve the validity of optimal results, uncertainty as a source of variability in the model’s output(s) need to be considered while this importance mostly has been ignored in designing of existing simulation optimization models. Under uncertainty, simulation running with stochastic output is complex in terms of computational time and/or cost, therefore the limited number of simulations is desirable. However, the accuracy of simulation result strongly depends on the reality of computer coding and discrepancy between simulation model and actual physical system. Most existing simulation optimization methods need to be improved in such a way to handle conflicting of multiple responses and constraints. This research generally aims to develop the black-box simulation optimization technique to be applicable in stochastic complex systems under effect of uncertainty with the least optimization computational burden (number of simulation experiments). This research develops a new distribution-free method for uncertainty management with unknown distribution of uncertainty. This research also aims to show the applicability and validity of proposed metamodel-based robust simulation optimization method in practical engineering design problems such as direct speed control of DC motor and PID tuning under uncertainty. For this purpose, metamodeling techniques are used for global approximation of complex simulation model. The statistical terminology of Taguchi crossed array design is replaced by global modern metamodels. A distribution-free method is suggested to tackle the lack of information about possible probability distribution of uncertainty scenarios in the model. Results of this research confirmed the validity and applicability of the proposed methodology dealing with practical stochastic complex engineering design problems in three terms; reducing computational time, enhancing flexibility, and improving the applicability. The proposed method can reduce the number of function evaluations for PID tuning under uncertainty to 50 simulation runs compared to more than 1000 function evaluations in common model based method. Compared to classical Ziegler Nichols method, the proposed method shows the better performance which is more than 10% for PID tuning under uncertainty. The proposed distribution–free method applied in economic order quantity problem shows the same accuracy compared to studies in literature whereby this study does not need to estimate distribution of uncertainty