Set Of Control Parameters Research Articles

Complex error minimization algorithm with adaptive change rate

In this paper, we develop a new algorithm for estimating control parameter values of ordinary differential equation (ODE) models. We call it the complex error minimization algorithm with an adaptive rate of change. It efficiently determines the parameters of ODE models even over extremely short time series. The algorithm is based on the gradient method and successfully solves its major drawback, namely getting stuck in local minima. To detect the getting stuck, we first introduce the calculation of four different errors at once. Our studies show that they achieved the global minimum only when we observe simultaneous minimization of all these errors. When getting stuck in local minima takes place, only one (or several) errors are minimized, while the others remain unchanged or grow. Thus, the algorithm accurately detects getting stuck in local minima. After this, it applies special methods based on simulated annealing to escape from local minima. The algorithm selects the most optimal path for convergence of parameter values to the global minimum by applying random restarts and multi-start methods to avoid local minima. We have tested our method on models with regular and chaotic dynamics and have found that it can work with high accuracy in these cases. Our method is suitable for actual systems with complex dynamics and an extensive set of control parameters but short time series. Thus, the proposed algorithm is an effective method for solving the inverse problem of ODE models.

Differential evolution with migration mechanism and information reutilization for global optimization

Differential evolution (DE) is an efficacious global optimization algorithm, and many variants have been advanced since its inception. During the iterative search process, any individual in any DE-type algorithm can likely locate a local optimum, and once that happens it may need many attempts for this individual to find another better solution, thus leading to ineffective consumption of computing resources and decline in the opportunity to search other promising regions. Therefor, this article proposes a DE with migration mechanism and information reutilization (MIDE). Specifically, a migration mechanism is first presented to make individuals located at local optima abandon current locations and move to other regions to continue their search, tending to solve the problem above. Meanwhile, a new mutation strategy named DE/pbest/1 with external archive is introduced to reutilize abandoned local optima to provide helpful information of evolution. Additionally, the settings of control parameters associated with this mutation strategy are designed in such a manner that they can contribute to well balancing exploration and exploitation. To evaluate the performance of MIDE, extensive experiments are conducted on CEC 2017 and CEC 2014 test suites, and the comparison results between MIDE and 19 competitors (including 13 state-of-the-art DE variants and six winner algorithms of CEC 2014 and CEC 2017 competitions) demonstrate MIDE's competitive performance.

Optimization of turning process parameters on EN8 unalloyed steel-based on MCDM methods

This research article is about optimizing turning parameters to find the best control parameter settings for maximum MRR and minimal tool wear. The experiment was performed on EN8 steel using the HSS S200 tool and input parameters were spindle speed, feed rate, depth of cut. Vegetable-based fluids were chosen as coolants for maintaining the sustainability of the operation.L9 orthogonal array design was used for making a combination set of 4 factors and 3 levels of control parameters. The Taguchi-based optimization technique was the subject of analysis with the TOPSIS, GRA & ARAS methods. The ideal control parameter settings for TOPSIS & GRA were discovered to be spindle speed 210 rpm, Feed rate 0.142 mm/rev, Depth of cut 1.5 mm, and Soya bean oil. For the ARAS method, it was found that 210 rpm spindle speed, 0.142 mm/rev feed rate, 1.0 mm depth of cut & Soya bean oil presented the optimal control parameter settings. Coolant shown maximum contribution over MRR & Tool wear. Finally the perfection of TOPSIS, GRA & ARAS results was validated by a prediction test & confirmation experiment.

Optimization of Abrasive Waterjet Cutting by Using the CODAS Method with Regard to Interdependent Processing Parameters

The paper shows performance optimization effects of steel machining by abrasive water jet (AWJ). An innovative combinative distance-based assessment method (CODAS) is implemented for the optimization of cutting parameters like pump pressure, feed rate, and abrasive flow rate over cutting depth, and cut kerf surface roughness. The CODAS algorithm is among those based on measuring the distance between a scenario (in this case processing parameters in terms of performance and quality indicators) - and a certain benchmark. A benchmark is a specific hypothetical set of processing parameters devised or determined from available data. To determine the best set of process control parameters, a CODAS approach was performed with some weighting determinations. To set the initial parameters of the weights, it was proposed to calculate based on entropy weight method (EWM), that measures output value dispersion in cutting process. This technique simplifies multiple compound responses by preserving a single response.

Nonlinear optimal control for permanent magnet synchronous spherical motors

Nonlinear optimal control for permanent magnet synchronous spherical motors

Novel proposal of PTO direct-drive linear generator, an Azimuthal Multi-translator Switched Reluctance Machine (AMSRM): mechanical, characterization and performance tests.

Research and development on power take-off (PTO) in wave energy has been gaining relevance in recent years, appearing as a priority in various forums (JCR reports [1], SRIA in wave energy development [2], specific research funding program in WES, etc.).
 The new proposal of a linear electric generator to act as a PTO in wave energy extraction applications has been developed in the European project SEATITAN [3].
 The linear generator is based on a switched reluctance machine type topology in which the magnetic flux is azimuthal. This allows eliminating the yoke, reducing the amount of magnetized material needed and increasing the force density, besides having a cylindrical shape that allows adapting perfectly to the spar of point absorber type WECs.
 The Azimuthal Multi-translator Switched Reluctance Machine (AMSRM) has been developed, designed and a prototype has been manufactured in the project. The prototype is composed of two modules of 35 kN of force and 3 m/s of peak velocity.
 The complete manuscript will detail the testing facility, as well as the tests performed for the static characterization of the force, electrical losses, and air gap achieved during manufacturing, as well as the dynamic characterization of frictional losses and a preliminary setting of force control parameters.
 The dry test facility (figure 1) consists of the necessary electronic converters to conveniently operate the generator and impose a reference force controlling current per generator coil. In addition, an end-stopper is provided to ensure the safety of the tests (this equipment is essential for dynamic tests). For static tests, a load cell for force measurement as well as precision electrical power meters are used.
 
 A test protocol has been defined for each test, as well as an analysis procedure to obtain the required results: for example, for the evaluation of friction, dynamic tests based on a back-and-forth motion with drifting sections are proposed to evaluate the braking force with the generator off at various speeds.
 The description of the test protocol, the raw data obtained, the data analysis carried out, and de results obtained will be presented and detailed in the manuscript, as well as the description of the test-based parameterization of a simplified AMSRM model to be used in the dynamic modeling of WECs. The simplified model has been previously developed and presented in EWTEC 2021.
 [1] D. Magagna, R. Monfardini, and A. Uihlein, “JRC Ocean Energy Status Report 2016 Edition,” 2016
 [2] J. L. Villate et al., “Strategic Research and Innovation Agenda for Ocean Energy,” Etipocean, no. 826033, p. 64, 2020.
 [3] WEDGE GLOBAL, “SeaTitan,” 2018. [Online]. Available: https://seatitan.eu/. [Accessed: 29-Nov-2021].
 [4] Marcos Blanco, Miguel Santos‐Herran, Gustavo Navarro, Jorge Jesus Torres, Jorge Najera, and Luis García-Tabarés, “Simplified model of a novel direct-drive PTO based on an azimuthal linear switched reluctance generator,” 2021.

Enhancement the Performance of Multi-Level and Multi-Commodity in Supply Chain: A Simulation Approach

In today's global economy, effective supply chain management plays a pivotal role in the success of a business. The repercussions of a business strategy on the entire supply chain remain uncertain until it is implemented. Utilizing simulations offers the opportunity to gauge performance before implementing the strategy. The primary aim of employing supply chain simulation is to analyze the effects of different strategies on profit enhancement and cost reduction across all supply chain tiers. This research paper has formulated a discrete event simulation model using Arena software to evaluate and enhance the operational efficiency of the detergent supply chain. The problem involves multiple levels and commodities, encompassing four manufacturers, two intermediate storage warehouses, and four main distributors following an (s, S) inventory control approach. Shortages are permitted, leading to a partial loss or back-ordering of products. The overarching objective is to minimize the overall inventory costs within the system, accounting for holding costs at each tier, managing shortages, and the expense incurred due to lost sales. A range of scenarios are developed to set control parameters, with the evaluation of supply chain performance falling into two main categories: financial and operational considerations.

Surrogate-assisted analysis of the parameter configuration landscape for meta-heuristic optimisation

Meta-heuristics can provide high-quality solutions to challenging problems in a reasonable amount of time, but are highly sensitive to the values assigned to their control parameters. The parameter configuration landscape (PCL) offers insight into the characteristics associated with optimisation of the parameter configuration of a meta-heuristic, but is poorly understood for most meta-heuristics. Further exacerbating this issue, determining the characteristics of the PCL is an extremely computationally expensive process. This study proposes the usage of artificial neural networks (ANNs) as surrogate models to greatly reduce the computational burden associated with characterising the PCL. Notably, this study represents the first usage of surrogate models in PCL research. Furthermore, this study presents a characterisation of the PCLs for both particle swarm optimization (PSO) and differential evolution (DE) employing ANN surrogate models, using five well-established fitness landscape analysis (FLA) metrics, and finds that the common assumption of correlation between the fitness and distance of control parameter settings is not strictly met. Overall, the training and usage of the surrogate models leads to a 99.86% reduction in the number of algorithm executions required to attain the PCL samples used in the characterisation.

Identifying suitable DC system models for an independent design of VSC controllers in MTDC grids

In AC-multi-terminal DC (MTDC) systems, if converter controllers are designed individually, the interactive system modes stemming from the dynamic couplings among the converters and the DC system components may result in the instability of the interconnected system (INTSYS). This paper presents a comparative system-level study to determine the suitable models for an individual design of converter controllers to prevent INTSYS instability. The goal is to identify suitable individual system (INDSYS) models that do not contain the internal dynamics of adjacent converters and only include the coupling dynamics corresponding to the interactive system modes among the converters. The use of these models for control design will result in the dynamic response of the INTSYS being close to that of the INDSYS models. Furthermore, such models can ensure INTSYS stability when converter controllers are designed individually without requiring the complete model of the INTSYS or internal dynamics of the adjacent converters. Both the master–slave and the droop control modes of operation are analyzed, and specific INDSYS models among those available in the literature are recommended for each operation mode. Time-domain simulations in PSCAD validate the accuracy of the INDSYS models. Furthermore, eigenvalue analysis along with the participation factor and sensitivity analyses for a large set of control parameters are employed to evaluate the efficacy of the recommended models.

Differential Evolution with Group-Based Competitive Control Parameter Setting for Numerical Optimization

Differential evolution (DE) is one of the most popular and widely used optimizers among the community of evolutionary computation. Despite numerous works having been conducted on the improvement of DE performance, there are still some defects, such as premature convergence and stagnation. In order to alleviate them, this paper presents a novel DE variant by designing a new mutation operator (named “DE/current-to-pbest_id/1”) and a new control parameter setting. In the new operator, the fitness value of the individual is adopted to determine the chosen scope of its guider among the population. Meanwhile, a group-based competitive control parameter setting is presented to ensure the various search potentials of the population and the adaptivity of the algorithm. In this setting, the whole population is randomly divided into multiple equivalent groups, the control parameters for each group are independently generated based on its location information, and the worst location information among all groups is competitively updated with the current successful parameters. Moreover, a piecewise population size reduction mechanism is further devised to enhance the exploration and exploitation of the algorithm at the early and later evolution stages, respectively. Differing from the previous DE versions, the proposed method adaptively adjusts the search capability of each individual, simultaneously utilizes multiple pieces of successful parameter information to generate the control parameters, and has different speeds to reduce the population size at different search stages. Then it could achieve the well trade-off of exploration and exploitation. Finally, the performance of the proposed algorithm is measured by comparing with five well-known DE variants and five typical non-DE algorithms on the IEEE CEC 2017 test suite. Numerical results show that the proposed method is a more promising optimizer.

Optimization of Circulating Fluidized Bed Boiler Combustion Key Control Parameters Based on Machine Learning

During the coal-fired circulating fluidized bed unit participation in the peak regulation process of the power grid, the thermal automatic control system assists the operator to adjust the mode focusing on pollutant control and ignoring the economy so that the unit’s operating performance maintains a huge potential for deep mining. The high-dimensional and coupling-related data characteristics of circulating fluidized bed boilers put forward more refined and demanding requirements for combustion optimization analysis and open-loop guidance operation. Therefore, this paper proposes a combustion optimization method that incorporates neighborhood rough set machine learning. This method first reduces the control parameters affecting multi-objective combustion optimization with the neighborhood rough set algorithm that fully considers the correlation of each variable combination and then establishes a multi-objective combustion optimization prediction model by combining the online calculation of boiler thermal efficiency. Finally, the NSGAII algorithm realizes the optimization of the control parameter setting value of the boiler combustion system. The results show that this method reduces the number of control commands involved in combustion optimization adjustment from 26 to 11. At the same time, based on the optimization results obtained by using traditional combustion optimization methods under high, medium, and medium-low load conditions, the boiler thermal efficiency increased by 0.07%, decreased by 0.02%, and increased by 0.55%, respectively, and the nitrogen oxide emission concentration decreased by 5.02 mg/Nm3, 7.77 mg/Nm3, and 7.03 mg/Nm3, respectively. The implementation of this method can help better account for the economy and pollutant discharge of the boiler combustion system during the variable working conditions, guide the operators to adjust the combustion more accurately, and effectively reduce the ineffective energy consumption in the adjustment process. The proposal and application of this method laid the foundation for the construction of smart power plants.

Stability Properties of the Constant Coefficients Semi-Implicit Time Schemes Solving Nonfiltering Approximation of the Fully Compressible Equations

Abstract A set of control parameters is introduced in the fully elastic nonhydrostatic Euler equations formulated in the mass-based vertical coordinate of Laprise. Contrary to the classical approach, the hydrostatic limit is represented by a subspace of control parameters, instead of a single point. By finding a suitable path from the fully compressible equations to the hydrostatic subspace, we are able to construct a blended system with acoustic modes slowed down and gravity modes nearly unaffected. Numerical stability of the discretized system is thus improved, and the solution remains essentially the fully compressible one. Alternatively, control parameters can be used to redefine the linear model of the constant coefficients semi-implicit time scheme, increasing the numerical stability of the fully compressible system. With a careful choice of the control parameters in both, the linear model used in the semi-implicit temporal scheme, and in the full model, the blended system does not deteriorate the compressible solution while its semi-implicit temporal discretization is more stable. We illustrate the potential of the method in several simple examples and in real case studies using the ALADIN system.

A nonlinear optimal control approach for voltage source inverter-fed three-phase PMSMs

A nonlinear optimal control approach for voltage source inverter-fed three-phase PMSMs

Online learning of MPC for autonomous racing

A Learning-based Model Predictive Control (LMPC) algorithm is proposed for a Formula Student (FS) autonomous vehicle. The online learning algorithm has two distinct roles: to improve the dynamic model accuracy of the vehicle used in the MPC, while performing online tuning of the model predictive controller parameters. The developed controller is shown to reduce the total lap time through an iterative learning process as the vehicle progresses on track. To capture the full complexity of the nonlinear higher order dynamics, an Artificial Neural Network (ANN) complements the vehicle’s nominal model. The ANN is trained using an online supervised learning scheme based on past model prediction errors. Additionally, a Genetic Algorithm (GA) is used to iteratively find the optimal set of controller parameters that maximizes a reward function. Several simulation tests performed on real examples of competition tracks demonstrate the effectiveness of the approach. Moreover, it is shown that the combination of both online learning methods is able to significantly improve tracking performance of the FS vehicle, eventually reducing the total lap time by over 16%.

Generative and discriminative training of Boltzmann machine through quantum annealing

A hybrid quantum-classical method for learning Boltzmann machines (BM) for a generative and discriminative task is presented. BM are undirected graphs with a network of visible and hidden nodes where the former is used as the reading site. In contrast, the latter is used to manipulate visible states’ probability. In Generative BM, the samples of visible data imitate the probability distribution of a given data set. In contrast, the visible sites of discriminative BM are treated as Input/Output (I/O) reading sites where the conditional probability of output state is optimized for a given set of input states. The cost function for learning BM is defined as a weighted sum of Kullback-Leibler (KL) divergence and Negative conditional Log-likelihood (NCLL), adjusted using a hyper-parameter. Here, the KL Divergence is the cost for generative learning, and NCLL is the cost for discriminative learning. A Stochastic Newton-Raphson optimization scheme is presented. The gradients and the Hessians are approximated using direct samples of BM obtained through quantum annealing. Quantum annealers are hardware representing the physics of the Ising model that operates on low but finite temperatures. This temperature affects the probability distribution of the BM; however, its value is unknown. Previous efforts have focused on estimating this unknown temperature through regression of theoretical Boltzmann energies of sampled states with the probability of states sampled by the actual hardware. These approaches assume that the control parameter change does not affect the system temperature; however, this is usually untrue. Instead of using energies, the probability distribution of samples is employed to estimate the optimal parameter set, ensuring that the optimal set can be obtained from a single set of samples. The KL divergence and NCLL are optimized for the system temperature, and the result is used to rescale the control parameter set. The performance of this approach, as tested against the theoretically expected distributions, shows promising results for Boltzmann training on quantum annealers.

Properties of the Petrinja (Croatia) earthquake sequence of 2020–2021 – Results of seismological research for the first six months of activity

We present locations of almost 14,000 events from the first six months of the Petrinja (Croatia) earthquakes sequence (mainshock 29 December 2020, Mw 6.4). The catalogue is estimated complete for ML ≥ 1.20. Initially sparse local seismograph network was densified a week after the mainshock, which reduced uncertainties of focal locations by about 75%. Source-specific station corrections were used in an iterative location scheme. The hypocentres were located with 30 different sets of velocity models and program control parameters in order to gain insight into the epistemic uncertainty of each earthquake location. The bulk of epicentres are located close to, and to the SE of the causative, dextral strike-slip Petrinja fault, but considerable activity has been triggered on smaller faults, up to 25 km away. About a half of the 78 first-motion polarity focal mechanism solutions (FMS) from the full first year of activity were found to have mechanisms similar to the foreshock, the mainshock and the largest of aftershocks, while the rest indicated almost pure reverse faulting. Strike-slip mechanisms occurred more often close to the main fault. The P-axes of FMS for aftershocks were found to be on the average rotated by 16° clockwise with respect to the P-axis of the mainshock, regardless of the style of faulting. The spatial distribution of aftershocks coincides very well with the areas of positive Coulomb stress change caused by the mainshock rupture. The inferred cut-off seismogenic depth in the area (7–10 km), estimated on the basis of published values of the geothermal gradient and the assumption of predominantly granitic upper crust, is considerably shallower than the depth of the deepest reliably located aftershocks (16–18 km). This fact may be explained by considering ophiolite-dominated crust, and by increased strain rate during the sequence, which raises the ability of crustal rocks for brittle failure.

Effect of Control Parameters on Hybrid Electric Propulsion UAV Performance for Various Flight Conditions: Parametric Study

Nowadays, great efforts of ongoing research are devoted to hybrid-electric propulsion technology that offers various benefits, such as reduced noise and pollution emissions and enhanced aircraft performance and fuel efficiency. The ability to estimate the performance of an aircraft in any flight situation in which it may operate is essential for aircraft development. In the current study, a simulation model was developed that allows estimating the flight performance and analyzing the mission of a fixed-wing multi-rotor Unmanned Aerial Vehicle (UAV) with a hybrid electric propulsion system (HEPS), with both conventional and Vertical Takeoff and Landing (VTOL) capabilities. The control is based on the continuous specification of pitch angle, propulsion thrust, and lift thrust to achieve the required conditions of a given flight segment. Six different missions were considered to analyze the effect of control parameters exhibiting the most influence on the UAV mission performance. An appropriate set of control parameters was selected through a multidimensional parametric study. The results show that the control parameters, if not well tuned, affect the mission performance: for example, in the deceleration transition, a longer time to reduce the cruise speed to stand still may be the result because the controller struggles to adjust the pitch angle. In addition, the implemented methodology captures the effects of transient maneuvers, unlike typical quasi-static analysis without the complexity of full simulation models.

An improved differential evolution algorithm for quantifying fraudulent transactions

Identification of fraudulent credit card transactions is a complex problem mainly due to the following factors: 1) The relative behavior of customers and fraudsters may alter over time. 2) The ratio of legitimate to fraudulent transactions is highly imbalanced, and 3) Investigators examine a small segment of transactions in a reasonable time frame. Researchers have proposed various algorithms to identify potential fraud in a new incoming transaction. However, these approaches require significant human investigator effort and are sometimes misleading. To address this issue, this paper proposes an improved multiobjective differential evolution (DE) algorithm to estimate the distribution of fraudulent transactions in a set of new incoming transactions, referred to as quantifying fraudulent transactions. Our paper has three major novelties. First, we present the problem formulation of cost-based feature selection with maximum quantification ability. Second, we improve the DE by applying effective trial vector generation algorithms to the random control parameter settings to exploit the advantage of individual DE variants. Third, we develop the maximum-relevancy-minimum-redundancy-based Pareto refining operator to enhance the self-learning ability of individuals in Pareto solutions. We compare our approach against four other modifications of DE and five state-of-the-art evolutionary algorithms on real-time credit datasets in streaming and non-streaming frameworks using hyper-volume, two-set coverage, and spread performance metrics.

Application of Optimal Network Reconfiguration for Loss Minimization and Voltage Profile Enhancement of Distribution System Using Heap-Based Optimizer

A distribution system’s network reconfiguration (NR) is the process of changing the status of the switches to change the topology of the feeders. Using optimal NR at various network load levels, this research proposed an economical way for improving the voltage profile and reducing power loss in distribution systems. A new metaheuristic Heap-based optimizer based on the corporate rank hierarchy is used to solve the optimization problem in order to find the optimal radial distribution network. The objective functions used in this article include voltage profile improvement, power loss reduction, reduction of operation cost, and minimization of gas emissions. This novel technique provides accurate results while avoiding the issue of premature convergence and requires fewer control parameter settings. The proposed method is evaluated at three different load levels on a standard IEEE 33-bus test system, and the simulation results are compared to those of other optimization methods published in the literature, demonstrating that the proposed method improves the system voltage profile while minimizing losses and power costs, establishing that this technique is effective in finding the best result. The Wilcoxon and Friedman tests are also used to verify the effectiveness of the proposed technique.

Neuromodulatory control of complex adaptive dynamics in the brain.

How is the massive dimensionality and complexity of the microscopic constituents of the nervous system brought under sufficiently tight control so as to coordinate adaptive behaviour? A powerful means for striking this balance is to poise neurons close to the critical point of a phase transition, at which a small change in neuronal excitability can manifest a nonlinear augmentation in neuronal activity. How the brain could mediate this critical transition is a key open question in neuroscience. Here, I propose that the different arms of the ascending arousal system provide the brain with a diverse set of heterogeneous control parameters that can be used to modulate the excitability and receptivity of target neurons-in other words, to act as control parameters for mediating critical neuronal order. Through a series of worked examples, I demonstrate how the neuromodulatory arousal system can interact with the inherent topological complexity of neuronal subsystems in the brain to mediate complex adaptive behaviour.