Optimal Set Of Control Parameters Research Articles

A framework for dynamical distributed flocking control in dense environments

The bio-inspired flocking model has been widely utilized in self-organized swarms. However, existing potential field methods fail to guarantee safe and orderly swarm movement in dense environments featuring both static and dynamic obstacles. In this study, we propose an innovative, dynamically optimized flocking control framework that integrates an improved and tunable self-propelled flocking model with an enhanced dynamic particle swarm optimization (DPSO) algorithm. Our model incorporates pigeon-inspired obstacle gap selection based on a probability approach to optimize steering decisions. Moreover, we introduce the DPSO algorithm, which combines a collaboration-based particle swarm optimizer with fractional order velocity incorporated history-guided estimation. This hybrid algorithm dynamically determines the optimal set of control parameters to maintain the desired swarm state. Through numerical comparison experiments with state-of-the-art models, we demonstrate that our approach enhances the safety, synchronization, and efficiency of the swarm even within a limited visual field, without prior environmental information. These features bring the simulations closer to real-world experiments.

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.

Neural network flow optimization using an oscillating cylinder

Flow behaviors of a downstream object can be affected significantly by an upstream object in close proximity. Combined with the neural network algorithms, this concept is used for flow control in this study to optimize the aerodynamic performance of a downstream object. Flow with an oscillating cylinder placed upstream is systematically studied because there are multiple control parameters that influence the flow dynamics around the downstream object. These control parameters are used as the input factors of a back-propagation neural network, and then a revised genetic algorithm is applied to find the optimal set of control parameters. In the current study, we use an airfoil in a low-Reynolds-number flow as an example to investigate the proposed neural network flow optimization concept. The datasets used to train the neural network are from the computational simulation with a previously validated immerse-boundary method to accommodate the motion of the cylinder. The results show that by optimally placing an upstream moving cylinder, it is possible to enhance the aerodynamic performance of the downstream object. Compared to the reference case, the optimized lift/drag ratio of the downstream airfoil can be achieved 2.4 times of its reference value, while maintaining a relatively high lift coefficient.

Desirability Function Analysis (DFA) in Multiple Responses Optimization of Abrasive Water Jet Cutting Process

This paper introduces optimization of machining parameters for high-pressure abrasive water jet cutting of Hardox 500 steel utilizing desirability function analysis (DFA). The tests were carried out according to the orthogonal matrix (Taguchi) L9. The control parameters of the process such as pressure, abrasive flow rate, and traverse speed was optimized under multi-response conditions namely cutting depth and surface roughness. The optimal set of control parameters was established on the basis of the composite desirability value obtained from desirability function analysis and the significance of these parameters was determined by analysis of variance (ANOVA). The effects show that optimal sets for high cutting depth and small surface roughness is high pressure, middle abrasive flow rate, and small traverse speed. A confirmation test was also leaded to validate the test results. Results of the research have shown that machining efficiency at keeping good level quality of cut surface can be improved this approach.

WASPAS Optimization in Advanced Manufacturing

This paper presented optimization of machining parameters for high pressure abrasive water jet (AWJ) cutting of Hardox 500 steel utilizing WASPAS approach. Weighted aggregated sum product assessment (WASPAS) method is explored as an effective MCDM tool while solving advanced manufacturing decision making problems. It is a unique combination of two well-known MCDM approaches, i.e. weighted sum model (WSM) and weighted product model (WPM).The tests were caried out according to the orthogonal matrix (Taguchi) L27. The control parameters of process such pressure, abrasive flow rate and traverse speed was optimized under multi-response conditions namely cutting depth and surface roughness. The optimal set of control parameters was established on the ground of the overall weighted criterium. Effects shows that optimal set for high cutting depth and small surface roughness are high pressure, high abrasive flow rate and small traverse speed. Results of research have shown that machining efficiency at keeping satisfactory level quality of cut surface can be improved this approach.

Optimization of process parameters in ultrasonic machining using integrated AHP-TOPSIS method

The process by virtue of which holes and cavities are formed in a metal work piece with the help of shaped tools, high frequency mechanical motion and abrasive slurry is known as Ultrasonic Machining Process. Its working principle is completely different from the other traditional machining processes such as laser beam machining and electrical discharge devices. This is developed machine removal processes in which the work material is less damaged by the thermal energy and it also introduce significant level of residual stress which is important for survival of the work material. This character of the process makes USM more enhanced and better machine job characteristics. This characteristic is also required for determining the optimal control parameter settings of an USM process’s is a non-traditional machining process in which abrasive content in slurry are driven against work by tool oscillating at low amplitude and high frequency. In this research paper stainless steel is used as tool material in the whole process and zirconia bio ceramic plate is taken as a work piece material. The process parameters are considered as feed rate, slurry concentration, power rating and grit size. Through this work, it is found that responses highly correlated with correlation coefficient, which justifies the application of MCDM as an optimization tool. The optimization studies are carried out on an ultrasonic machining process with multi response characteristics depend on the multi criteria decision making methodology (MCDM), using technique for order preference by similarity to ideal solution (AHP-TOPSIS) approach are described in this paper. The final results of this paper indicate the effectiveness of USM using MCDM method for different USM parameters to produce high quality parts made of zirconia Bio-ceramics with excellent productivity.

Osteosynthesis of diaphyseal tibia fracture with locking compression plates: A numerical investigation using Taguchi and ANOVA.

Performance of the locking compression plate (LCP) is a multifactorial function. The control parameters of plating, such as geometries, material properties, and physical constraints of the LCP components, affect basic functions associated with the bone fixation, including the extent of stress shielding and subsequent bone remodeling, strength and stability of the bone-LCP construct, and performance of secondary bone healing. The main objectives of this research were as follows: (1) to find the appropriate values of control parameters of an LCP construct to achieve the optimized performance throughout bone healing; and (2) to unravel relationships between LCP parameters and the LCP's performance. Different values for the plate/screw modulus of elasticity (E), plate width (W), plate thickness (T), screw diameter (D), bone-plate offset (O), and screw configuration (C), as six control parameters, were considered at five different levels. Taguchi method was adopted to create trial combinations of control parameters and determining the best set of parameters, which can optimize the overall performance of the LCP. All design cases were analyzed using the finite element method. The optimal set of control parameters consisting of 150 GPa, 12 mm, 4 mm, 5.5 mm, 2 mm, and 123,678 were determined for E, W, T, D, O, and C, respectively. Furthermore, ANOVA was used to rank the most influential parameters on each function of the LCP fixation. In the overall performance of the LCP fixation, E, D, T, C, W, and O showed a contribution percentage of 46%, 22%, 10%, 11%, 8%, and 3%, respectively.

Investigation on electric erosion behavior of nickel-based super alloy (Waspaloy: Ni, Cr, Co, Mo, Ti, Al) using response surface methodology

Waspaloy is one of the nickel-based alloys and its application is mostly positioned in the aerospace industry owing to its strength retaining capacity at higher temperature, better corrosion resistance, and toughness. Multi-response optimization technique, response surface methodology (RSM) was adopted to optimize the electrical discharge machining parameters during machining of Waspaloy. Various control factors such as peak current, voltage, duty cycle, and flushing pressure were selected with three levels. The material removal rate (MRR) and surface roughness (SR) were selected as response parameters for investigating the machining characteristics. A central composite design with five center points was used for experimental design. The best optimal set of control parameters were identified for the higher MRR and lower Sr The experiments have been validated by using analysis of variance and a confirmation test was conducted. From RSM, the optimum machining conditions for drilling the hole, namely, voltage: 125 V, flushing pressure: 0.8 kg cm−2, and peak current: 22 A and duty cycle of 20% have been identified. Further, this investigation highlights the formation and the effect of the recast layer by using scanning electron microscopy.

Recursive Gaussian Process-Based Adaptive Control, With Application to a Lighter-Than-Air Wind Energy System

This brief presents a nonmodel-based adaptive control technique that combines principles from machine learning and iterative design optimization with those of continuous-time, falsification-based adaptive control. At the crux of the proposed control strategy are two core elements. First, the recursive Gaussian Process (RGP) modeling is used to maintain an online characterization of the system at hand without the need to maintain a complete database of previously collected measurements (which is required in traditional GP modeling). Second, an adaptation strategy is employed that falsifies candidate controllers from a continuous candidate design space based on desired performance specifications and statistical hypothesis testing. In specific, the control parameter design space is explored by selecting points associated with high uncertainty. Through the use of statistical hypothesis testing, regions of the design space determined to be suboptimal at a user-specified level of confidence are rejected in order to converge to an optimal set of control parameters. The RGP-based adaptation is validated through simulations and laboratory-scale experiments using an airborne wind energy case study. Through these studies, the RGP-based adaptation approach is shown to be effective and is shown to exhibit favorable convergence times when compared with a mature adaptive control technique, extremum seeking (ES).

Fuzzy multi-objective approach-based small signal stability analysis and optimal control of a PMSG-based wind turbine

The objective of this manuscript is to design a controller to enhance the degree of stability through small signal analysis in case of a grid connected permanent magnet synchronous generator (PMSG)-based wind turbine and to ensure an optimal set of control parameters to achieve an enhanced performance. The optimal control parameters are computed by optimising the placement of system eigenvalues and net errors by formulating a fuzzy-based multi-objective approach. The idea behind the formulation of the objective function through a multi-objective approach involves the association of error with relative stability of the system through computation of the real parts of eigenvalues. To find the optimal control gains, a two-fold mutation-based differential evolution optimisation is used. Results from a MATLAB-based model are presented for validation of the proposed technique to demonstrate the system stability when subjected to wind speed variation.

Assessment of optimized calibrations in minimizing GHG emissions from a Dual Fuel NG/Diesel automotive engine

Natural gas is recognized as a valid alternative to conventional fuels for internal combustion engines, thanks to its characteristics of low environmental impact and considerable natural reserves. In a Dual Fuel (DF) NG/Diesel configuration, NG offers interesting possibilities in terms of nitrogen oxides and soot emissions abatement, with reduced CO2 levels. Despite a proven feasibility of the DF concept for heavy duty engines, its applicability to light duty engines still clashes with several issues, especially the high unburned hydrocarbons emission.The present activity was devoted to assess the potentiality of an optimized DF engine calibration, to minimize GHGs emissions. A first experimental campaign on an automotive Diesel engine, equipped with an advanced combustion control, illustrated the effects of crucial control parameters on DF combustion. Hence, an optimization procedure permitted to identify the optimal set of control parameters. Finally, experiments in steady state and transient conditions allowed quantifying the THC and CO2 emissions reductions achievable moving from a starting DF engine map to the optimized calibration.The investigations highlighted that an adequate engine recalibration can offer a CO2 saving of about 12%, compared to the standard Diesel configuration, whereas the detrimental effect in terms of THC emissions could not be fully eliminated. Nevertheless, compared to a non-recalibrated DF map, the engine parameters optimization allowed achieving a THCs reduction of roughly 50%.Reported results could provide valuable information on behavior tendencies of a DF engine, useful for the development of dedicated engine technologies for DF combustion systems.

Generation of a Clocking Signal in Synchronized All-Digital PLL Networks

In this brief, we propose a discrete-time framework for the modeling and studying of all-digital phase-locked loop (ADPLL) networks with applications in clock-generating systems. The framework is based on a set of nonlinear stochastic iterating maps and allows us to study a distributed ADPLL network of arbitrary topology. We determine the optimal set of control parameters for the reliable synchronous clocking regime, taking into account the intrinsic noise from both local and reference oscillators. The simulation results demonstrate very good agreement with experimental measurements of a 65-nm CMOS ADPLL network. This brief shows that an ADPLL network can be synchronized both in frequency and phase. We show that for a large Cartesian network the average network jitter increases insignificantly with the size of the system.

Component Swapping Modularity for Distributed Precision Contouring

Prior works use numerical simulations to verify component swapping modularity (CSM) for various systems. Moreover, current CSM algorithms do not investigate control parameter allocation for a realistic control configuration when the control consists of multiple parts. Thus, this work primarily focuses on presenting and experimentally validating an empirical methodology to allocate and calibrate various parts of the controller to improve CSM in a precision multiaxis servo-system. As a secondary contribution, the concept of the modified reference contour is introduced to simplify control allocation and to improve the modularity of the cross-coupling control (CCC), which is utilized to achieve high-precision contouring. First, the unified linear CCC algorithm is presented for multiaxis servo-systems. Initially, the sensitivity of the contouring algorithm versus the control parameters is studied numerically. Then, empirical calibration and sensitivity analysis are conducted to obtain the optimal set of control parameters. Hence, the lowest feasible contour error is obtained for possible configurations of the servo-system. It is shown that the results of the empirical analysis are consistent with those of the numerical analysis. Despite dramatic differences between the servo-system variants, experimental results show that full CSM is achieved with the same controller for the variants of the servo-system.

Optimal Control for Fast and Robust Generation of Entangled States in Anisotropic Heisenberg Chains

Motivated by some recent results of the optimal control (OC) theory, we study anisotropic XXZ Heisenberg spin-1/2 chains with control fields acting on a single spin, with the aim of exploring how maximally entangled state can be prepared. To achieve the goal, we use a numerical optimization algorithm (e.g., the Krotov algorithm, which was shown to be capable of reaching the quantum speed limit) to search an optimal set of control parameters, and then obtain OC pulses corresponding to the target fidelity. We find that the minimum time for implementing our target state depending on the anisotropy parameter Δ of the model. Finally, we analyze the robustness of the obtained results for the optimal fidelities and the effectiveness of the Krotov method under some realistic conditions.

Calibration efficiency improvement of rule-based energy management system for a plug-in hybrid electric vehicle

This paper presents a calibration method of a rule-based energy management strategy designed for a plug-in hybrid electric vehicle, which aims to find the optimal set of control parameters to compromise within the conflicting calibration requirements (e.g. emissions and economy). A comprehensive evaluating indicator covering emissions and economy performance is constructed by the method of radar chart. Moreover, a radial basis functions (RBFs) neural network model is proposed to establish a precise model within the control parameters and the comprehensive evaluation indicator. The best set of control parameters under offline calibration is gained by the multi-island genetic algorithm. Finally, the offline calibration results are compared with the experimental results using a chassis dynamometer. The comparison results validate the effectiveness of the proposed offline calibrating approach, which is based on the radar chart method and the RBF neural network model on vehicle performance improvement and calibrating efficiency.

Metamodeling and Optimization of a Blister Copper Two-Stage Production Process

It is often difficult to estimate parameters for a two-stage production process of blister copper (containing 99.4 wt.% of Cu metal) as well as those for most industrial processes with high accuracy, which leads to problems related to process modeling and control. The first objective of this study was to model flash smelting and converting of Cu matte stages using three different techniques: artificial neural networks, support vector machines, and random forests, which utilized noisy technological data. Subsequently, more advanced models were applied to optimize the entire process (which was the second goal of this research). The obtained optimal solution was a Pareto-optimal one because the process consisted of two stages, making the optimization problem a multi-criteria one. A sequential optimization strategy was employed, which aimed for optimal control parameters consecutively for both stages. The obtained optimal output parameters for the first smelting stage were used as input parameters for the second converting stage. Finally, a search for another optimal set of control parameters for the second stage of a Kennecott–Outokumpu process was performed. The optimization process was modeled using a Monte-Carlo method, and both modeling parameters and computed optimal solutions are discussed.

Development of reversible bonding for microfluidic applications

Reversible bonding that allows repeating assembly and disassembly of microfluidic devices is very useful for a number of applications such as surface functionalization, complex cell patterning, and other biological analysis. However, reversible microfluidic devices fabricated with the current standard procedures can only be used for low-pressure applications. In this paper, we describe and characterize a reliable, flexible, and reversible bonding technique of PDMS–PDMS (Poly-dimethyl siloxane) using an oxygen plasma treatment. Effects of control parameters, such as the thickness of the PDMS layer, the duration and power of the plasma treatment, the duration and temperature of the thermal treatment on the quality of the obtained devices are investigated. An optimal set of control parameters enabling the obtained devices to work at high flow rates and pressures (500 µL/min and 148 kPa) has been determined. Furthermore, the disassembly/assembly process of the device can be repeated up to four times.

Parametric optimization of ultrasonic machining process using gravitational search and fireworks algorithms

Abstract Ultrasonic machining (USM) is a mechanical material removal process used to erode holes and cavities in hard or brittle workpieces by using shaped tools, high-frequency mechanical motion and an abrasive slurry. Unlike other non-traditional machining processes, such as laser beam and electrical discharge machining, USM process does not thermally damage the workpiece or introduce significant levels of residual stress, which is important for survival of materials in service. For having enhanced machining performance and better machined job characteristics, it is often required to determine the optimal control parameter settings of an USM process. The earlier mathematical approaches for parametric optimization of USM processes have mostly yielded near optimal or sub-optimal solutions. In this paper, two almost unexplored non-conventional optimization techniques, i.e. gravitational search algorithm (GSA) and fireworks algorithm (FWA) are applied for parametric optimization of USM processes. The optimization performance of these two algorithms is compared with that of other popular population-based algorithms, and the effects of their algorithm parameters on the derived optimal solutions and computational speed are also investigated. It is observed that FWA provides the best optimal results for the considered USM processes.

Parameter Selection for a Centralized Thermostatically Controlled Appliances Load Controller Used for Intra-Hour Load Balancing

This paper presents a novel dynamic parameter selection process to optimize the performance of a centralized load controller designed to provide intra-hour load balancing services using thermostatically controlled appliances (TCAs). An optimal set of control parameters for the controller are selected by exhaustive simulations of control variables such as the sampling time of the forecaster, the magnitude of the load balancing signal, and the temperature deadband. The effects of TCA lock-off times, ambient temperatures, heat gains, and two-way communication delays on the controller design are also modeled. Customer comfort, device life cycles, and control errors are used as metrics to evaluate the performance. The results demonstrate that the optimized controller offers satisfactory performance considering all the operational uncertainties.

Application of Taguchi Method for the Optimum Design of a 3MW Permanent Magnet Synchronous Generator

The paper is examined to establish the optimal set of control parameters for the design of a 3MW direct-drive permanent magnet synchronous generator by applying the Taguchi method. Taguchi orthogonal array is employed to determine the optimal combination of design parameters, including: steel lamination material, applied voltage, slot number, diameter of air-gap, magnet pole materials, magnet pole depth, stator outer/inner diameter, and length of stator core under the rated rotating speeds of the generator. Next, core loss database of used electrical steel laminations in generator was investigated. Effect of these control parameters of the permanent-magnet synchronous generator on the output power and efficiency are also analyzed in the paper. Results show that the designed 3MW direct-drive permanent magnet synchronous generator is capable of operating stably and assure the adequate 3MW power output and good efficiency greater than 95%.