Set Of Control Parameters Research Articles

Adaptive Ant Colony Optimization with node clustering applied to the Travelling Salesman Problem

This article presents the Ant Colony Optimization algorithm to solve the Travelling Salesman Problem. The proposed algorithm implements three novel techniques to enhance the overall performance, lower the execution time and reduce the negative effects particularly connected with ACO-based methods such as falling into a local optimum and issues with settings of control parameters for different instances. These techniques include (a) the node clustering concept where transition nodes are organised in a set of clusters, (b) adaptive pheromone evaporation controlled dynamically based on the information entropy and (c) the formulation of the new termination condition based on the diversity of solutions in population. To verify the effectiveness of the proposed principles, a number of experiments were conducted using 30 benchmark instances (ranging from 51 to 2,392 nodes with various nodes topologies) taken from the well-known TSPLIB benchmarks and the results are compared with several state-of-the-art ACO-based methods; the proposed algorithm outperforms these rival methods in most cases. The impact of the novel techniques on the behaviour of the algorithm is thoroughly analysed and discussed in respect to the overall performance, execution time and convergence.

Reachable Set Synthesis of Markov Jump Systems With Time-Varying Delays and Mismatched Modes

This note addresses the problem of reachable set synthesis of discrete-time Markov jump systems (MJSs) with time-varying delays and mismatched modes. The objective is to design suitable controllers to ensure the reachable set of the resulting closed-loop MJS is bounded by a prescribed set. First, by taking time-varying delays and mismatched modes into consideration, an ellipsoidal description of the reachable set is presented, which includes actual reachable states of the closed-loop MJS starting from the origin. Then, based on the description above, asynchronous reachable set controller parameters can be obtained if a set of linear matrix inequalities are feasible. Finally, an example is given to verify the validity of the obtained results.

Tuning of PID Control for the Double Integrator Plus Dead Time Model by Modified Real Dominant Pole and Performance Portrait Methods

The paper discusses the proportional-integral-derivative (PID) controller from the viewpoint of (a) the analytical tuning of the PID controller for the double integrator plus dead time (DIPDT) model and (b) the numerical tuning using the performance portrait method (PPM). In the first case, the already published tuning with multiple real dominant pole, extended by integrated tuning procedures, which incorporate the inevitable low-pass filters by delay equivalences, is elaborated for modified sets of real poles. By considering several such modified sets of real poles, resulting in several new sets of controller parameters, the design can be better adapted to the requirements of the control tasks solved and to the limitations of the existing control loop hardware. In a noisy and uncertain environment, the balance between speed of setpoint and disturbance responses and acceptable excessive controller effort can thus be improved. The effectiveness of the analytical design can be evaluated using the numerical performance portrait method (PPM). For an already generated performance portrait (PP), it can offer a broad spectrum of controller settings that satisfy various design constraints. However, the results of the analytical design are still important as they facilitate the initial steps in the elaboration of the PPM and in explaining the nature of PID control. The developed controller tuning are compared using a new interpretation of PID controller as an extension of the stabilising PD controller by disturbance observer (DOB). The input disturbances reconstructed by DOB by evaluating the controller output of an integral process model in steady-state, can be estimated by a low-pass filter with a sufficiently long (integral) time constant. All analysed results are in full agreement with the proposed DOB interpretation, which furthermore contributes significantly to the explanation of the problems related to the optimal design of PID controllers.

Evolutionary background-oriented schlieren tomography with self-adaptive parameter heuristics.

For volumetric reconstruction of the refractive index field in a flow, background-oriented schlieren (BOS) imaging which measures the deflection of light rays due to refractive index variations is combined with an evolutionary tomographic algorithm for the first time, called evolutionary BOS tomography (EBOST). In this work application to reactive flows is presented. Direct non-linear ray-tracing of the reconstruction domain is used to evaluate the fitness of solution candidates during the evolutionary strategy that was implemented to run on a multi-GPU system. The use of a diversity measure and its consideration in a migration policy was tested against a simple scheme that distributes the best chromosome (solution candidate) in an island-based genetic algorithm. The extensive set of control parameters of the presented algorithm was harnessed by a self-adaptive strategy taking into account the fitness function and operator rates. Quantitative characterisation of the EBOST via numerical phantom studies, using flame simulations as ground truth data is presented. A direct comparison to a state-of-the-art BOST algorithm demonstrates similar accuracy for a turbulent swirl flame phantom reconstruction. A series of experimental applications of the EBOST on several unsteady and turbulent flames is also presented. In all cases, the instantaneous and time-averaged flame structure is revealed, proving the benefit of EBOST for volumetric flow diagnostics.

Shaping sustainable harvest boundaries for marine populations despite estimation bias

AbstractBiased estimates of population status are a pervasive conservation problem. This problem has plagued assessments of commercial exploitation of marine species and can threaten the sustainability of both populations and fisheries. We develop a computer‐intensive approach to minimize adverse effects of persistent estimation bias in assessments by optimizing operational harvest measures (harvest control rules) with closed‐loop simulation of resource‐management feedback systems: management strategy evaluation. Using saithe (Pollachius virens), a bottom water, apex predator in the North Sea, as a real‐world case study, we illustrate the approach by first diagnosing robustness of the existing harvest control rule and then optimizing it through propagation of biases (overestimated stock abundance and underestimated fishing pressure) along with select process and observation uncertainties. Analyses showed that severe biases lead to overly optimistic catch limits and then progressively magnify the amplitude of catch fluctuation, thereby posing unacceptably high overharvest risks. Consistent performance of management strategies to conserve the resource can be achieved by developing more robust control rules. These rules explicitly account for estimation bias through a computational grid search for a set of control parameters (threshold abundance that triggers management action, Btrigger, and target exploitation rate, Ftarget) that maximize yield while keeping stock abundance above a precautionary level. When the biases become too severe, optimized control parameters—for saithe, raising Btrigger and lowering Ftarget—would safeguard against a overharvest risk (<3.5% probability of stock depletion) and provide short‐term stability in catch limit (<20% year‐to‐year variation), thereby minimizing disruption to fishing communities. The precautionary approach to fine‐tuning adaptive risk management through management strategy evaluation offers a powerful tool to better shape sustainable harvest boundaries for exploited resource populations when estimation bias persists. By explicitly accounting for emergent sources of uncertainty, our proposed approach ensures effective conservation and sustainable exploitation of living marine resources even under profound uncertainty.

A Bio-Inspired Multi-Population-Based Adaptive Backtracking Search Algorithm.

Backtracking search algorithm (BSA) is a nature-based optimization technique extensively used to solve various real-world global optimization problems for the past few years. The present work aims to introduce an improved BSA (ImBSA) based on a multi-population approach and modified control parameter settings to apprehend an ensemble of various mutation strategies. In the proposed ImBSA, a new mutation strategy is suggested to enhance the algorithm’s performance. Also, for all mutation strategies, the control parameters are updated adaptively during the algorithm’s execution. Extensive experiments have been performed on CEC2014 and CEC2017 single-objective benchmark functions, and the results are compared with several state-of-the-art algorithms, improved BSA variants, efficient differential evolution (DE) variants, particle swarm optimization (PSO) variants, and some other hybrid variants. The nonparametric Friedman rank test has been conducted to examine the efficiency of the proposed algorithm statistically. Moreover, six real-world engineering design problems have been solved to examine the problem-solving ability of ImBSA. The experimental results, statistical analysis, convergence graphs, complexity analysis, and the results of real-world applications confirm the superior performance of the suggested ImBSA.

Study on the oscillation transmission paths in direct‐drive wind farm transmitted via flexible dc system based on dynamic energy flow

Concerning the sub-synchronous oscillation induced by the interaction among wind power converter, flexible DC converter and multiple control links, a method to study the oscillation transmission path based on dynamic energy flow is proposed. First, the system is divided into several subsystems according to different control links, and the dynamic energy model of each subsystem is built by using the first integration method. Then, the energy flows induced by different control links are determined according to the corresponding oscillation components. On this basis, the intersection of energy flows in each dynamic energy model is defined as an energy converging point. Thus, the energy flows between different energy converging points are obtained, and the oscillation transmission path of the interconnected system between different subsystems is determined. Finally, combining the parameters of a real direct-drive wind farm transmitted via flexible DC system, a test system is built in RT-LAB for simulation verification. The results show that the proposed method can describe the oscillation transmission paths between different control links quantitatively, and reveal the control links that make against the stability of system. Besides, studying the energy distribution in the oscillation transmission paths can contribute to further research on the control parameter setting and energy suppression, and lay the theoretical foundation for the design of supplementary compensation branches.

Distributed sensing via the ensemble spectra of uncoupled electronic chaotic oscillators

An electronic chaos generator involving cross-coupled inverter rings with lengths equal to the smallest odd prime numbers is considered, and a spatiotemporally-varying control parameter, hypothetically corresponding to a physical variable to be sensed, is introduced akin to a baseband signal. It is shown that, owing to the fine-grained structure of the spectrogram conveniently generated by this circuit, the control parameter setting and its fluctuations can be accurately estimated from the frequency spectrum, or a portion of it, for instance using a small neural network. Mimicking the effect of a passive receiver listening to a set of such nodes, the effect of summing the partially spectrally-overlapping and largely incoherent signals arising from multiple unsynchronized transmitters is then considered. The possibility of estimating some features of the statistical and topographical distribution of the control parameter from the frequency spectrum of the population ensemble signal(s) is indicated. Physical feasibility is corroborated by experiments involving an integrated circuit prototype realized using 65 nm CMOS technology, through which measurement reproducibility is also assessed. The proposed approach may have advantages for realizing distributed sensing applications because synchronization among the sensor nodes is not required, allowing the use of considerably simpler transmitters rather than mutually-coupled oscillators.

Optimal Controllers to Improve Transient Recovery of Grid-Following Inverters Connected to Weak Power Grids

Leading to the enormous growth in renewable and power electronics technologies and the global drive towards environmental friendliness and sustainability, a significant number of renewable energy sources are being connected to the power system via inverter-based systems. The inverter-based generations (IBG) have no stored energy and less fault current injection capability compared to the conventional synchronous machines. Consequently, a large penetration of IBG creates challenges to maintaining the stability of the power system, especially the transient stability. The weaker the power system, the higher the significance of instability. Few solutions exist in the literature to improve the fault recovery of IBG connected to weak power systems. This paper considers the method of storing energy in sub-module capacitors of the Modular-Multi-level Converter (MMC) along with temporarily boosting the inverter’s current limit. Conversely, increasing the ratings of the inverter will result in high manufacturing costs. Hence an optimization strategy is proposed in this paper, for obtaining a robust set of inverter control parameters that enhances fault recovery without excessively increasing the manufacturing cost of MMC. A frequency scanning technique supplemented with Generalized Nyquist criteria is incorporated into the optimization methodology to constrain the search space for the optimization algorithm. This enables the optimization algorithm to converge to an acceptable solution with a reasonable computing time. Furthermore, validation of the resultant set of parameters for different system conditions is presented. Finally, IBG with optimized fault recovery controllers is integrated into a simplified real-world power system, and the applicability of the proposed optimized controllers is illustrated.

Performance Evaluation of Mobility Robustness Optimization (MRO) in 5G Network With Various Mobility Speed Scenarios

The massive deployment of small-sized cells for the Fifth Generation (5G) mobile network will increase the Handover Probability (HOP), potentially causing higher Handover Ping-Pong Probability (HPPP) and/or Radio Link Failure (RLF). Inappropriate usage of Handover Control Parameter (HCP) settings may further exasperate this issue. Therefore, Mobility Robustness Optimisation (MRO) has been introduced and further developed as a significant Self-Optimisation Network (SON) function in the 5G network and beyond. The main aim of MRO is to address Mobility Management (MM) issues during user mobility between cells to ensure a smooth connection. Although various algorithms were suggested in the literature, they mostly cater to 4G networks which may not be effective for the 5G network due to different network characterisations. This paper analyses the performance of various MRO algorithms with various system settings and scenarios for the 5G network. The investigated algorithms from the literature include the Distance (Dis), Cost Function (CF), Fuzzy Logic Controller (FLC) and Handover Performance Indicator (HPI). Validation has been accomplished for different mobility conditions in the 5G network. A simulation based on the MATLAB software has been conducted using various system tools. The evaluation analysis is in terms of Signal to-Interference-plus-Noise-Ratio (SINR), HPPP and RLF effects since these are major indicators in assessing system performance and selecting the handover decision during user mobility. The simulation outcomes show that the HPI algorithm performance is more reactive to mobile speed scenarios over time, significantly reducing the HPPP compared to the other algorithms which do not provide large reactions in the same conditions. Simultaneously, the HPI algorithm exhibits the highest RLF and SINR from among the other algorithms. The distance algorithm is the best in terms of RLF and SINR, achieving an acceptable level in terms of HPPP. These results point to that the MRO algorithm that operate based on distance is the most robust compared to the other investigated algorithms, confirming the potential of the Dis approach for the 5G network.

Distributed Cluster Consensus of Higher-Order Linear Continuous-Time Systems with Input Delay

This paper studies the higher-order cluster consensus problem for linear multi-agent systems with fixed input delay. It is proved that there is always a non-empty set of control parameters that makes a delayed third-order system stable. In light of this result, a control parameter selection procedure is proposed for higher-order systems under fixed input delay. Theoretical results are verified by numerical examples.

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.

Layering and vertical transport in sheared double-diffusive convection in the diffusive regime

A sequence of two- and three-dimensional simulations are conducted for the double-diffusive convection (DDC) flows in the diffusive regime subjected to an imposed shear. For a wide range of control parameters, and for sufficiently strong perturbation of the conductive initial state, staircase-like structures spontaneously develop, with relatively well-mixed layers separated by sharp interfaces of enhanced scalar gradient. Such staircases appear to be robust even in the presence of strong shear over very long times, with early-time coarsening of the observed layers. For the same set of control parameters, different asymptotic layered states, with markedly different vertical scalar fluxes, can arise for different initial perturbation structures. The imposed shear significantly spatio-temporally modifies the vertical transport of the various scalars. The flux ratio $\gamma ^*$ (i.e. the ratio between the density fluxes due to the total salt flux and the total heat flux) is found, at steady state, to be essentially equal to the square root of the ratio of the salt diffusivity to the thermal diffusivity, consistent with the physical model proposed by Linden & Shirtcliffe (J. Fluid Mech., vol. 87, 1978, pp. 417–432) and the variational arguments presented by Stern (J. Fluid Mech., vol. 114, 1982, pp. 105–121) for unsheared double-diffusive convection.

Operating a passive on-chip superconducting circulator: Device control and quasiparticle effects

Microwave circulators play an important role in quantum technology based on superconducting circuits. The conventional circulator design, which employs ferrite materials, is bulky and involves strong magnetic fields, rendering it unsuitable for integration on superconducting chips. One promising design for an on-chip superconducting circulator is based on a passive Josephson-junction ring. In this paper, we consider two operational issues for such a device: circuit tuning and the effects of quasiparticle tunneling. We compute the scattering matrix using adiabatic elimination and derive the parameter constraints to achieve optimal circulation. We then numerically optimize the circulator performance over the full set of external control parameters, including gate voltages and flux bias, to demonstrate that this multi-dimensional optimization converges quickly to find optimal working points. We also consider the possibility of quasiparticle tunneling in the circulator ring and how it affects signal circulation. Our results form the basis for practical operation of a passive on-chip superconducting circulator made from a ring of Josephson junctions.

High-frequency voltage-driven vibrations in dielectric elastomer membranes

This article deals with modelling and experimental characterisation of the continuum dynamic response of dielectric elastomer actuators (DEAs) subject to high-frequency voltage excitation.DEAs are capable of large deformations in response to an electrostatic stimulus, and they show large operating bandwidths of up to a few kilohertz. Although DEA systems normally make use of simple deformation patterns and a well-defined main actuation mode, they show complex structural dynamics and mode shapes if subject to high frequency voltage inputs. Taking advantage of these complex structural dynamics potentially allows developing multi-function actuators, audio devices and vibration isolators capable to perform different tasks using different vibration regimes.We first present a multi-domain model for the structural dynamics of DEAs, accounting for the contribution of the air pressure loads generated the DEA membrane vibrations, which play a relevant role in the DEA dynamics.We then present an extensive experimental characterisation of DEA samples’ structural dynamics based on laser Doppler vibrometer measurements. In particular, we measure the complex structural mode shapes and the velocity spectra generated through a broadband excitation in correspondence of a wide set of different design and control parameters (namely, the DEA geometric layout, the mechanical-preload and the applied voltage bias).Based on the experimental results, we validate the proposed continuum model, and we demonstrate that the forced response of the DEA can be efficiently described using a lumped-parameter computationally efficient reduced reformulation.

Modeling and Design of Parallel LCC-VSC Interlinking Converters’ Unified Controller in AC/DC Network

While both line-commutated converters (LCCs) and voltage-source converters (VSCs) have been adopted in ac/dc grids, they are mostly used individually despite some point-to-point dc transmission. The combination of both technologies as a single interlinking converter (IC) unit in an ac/dc grid can bring advantages, such as capacity expansion, bidirectional power flow, reactive power support, and harmonics compensation. For such a parallel LCC-VSC unit, a suitable control scheme and careful control parameters’ design are important to ensure that the system can maintain the above advantages in different operation modes. In this article, the control scheme and parameter design for a parallel LCC-VSC unit for use in an ac/dc grid are conducted with a focus on capacity expansion scenario, where a unified control scheme that reserves most of the original LCC terminal’s control while also smoothly incorporates the VSC operation is considered. The operation states of a hybrid ac/dc network are modeled. The proposed unified control scheme is considered for each state for the stability study. Then, a set of control parameters for the unified control scheme suitable for all operation states with good dynamics and stability is designed. Real-time simulations and experiments are conducted to verify the design and study.

Optimizing Connection Weights in Neural Networks Using Hybrid Metaheuristics Algorithms

The learning process of artificial neural networks is an important and complex task in the supervised learning field. The main difficulty of training a neural network is the process of fine-tuning the best set of control parameters in terms of weight and bias. This paper presents a new training method based on hybrid particle swarm optimization with Multi-Verse Optimization (PMVO) to train the feedforward neural networks. The hybrid algorithm is utilized to search better in solution space which proves its efficiency in reducing the problems of trapping in local minima. The performance of the proposed approach was compared with five evolutionary techniques and the standard momentum backpropagation and adaptive learning rate. The comparison was benchmarked and evaluated using six bio-medical datasets. The results of the comparative study show that PMVO outperformed other training methods in most datasets and can be an alternative to other training methods.

SFSADE: an improved self-adaptive differential evolution algorithm with a shuffled frog-leaping strategy

The differential evolution (DE) algorithm is an efficient random search algorithm based on swarm intelligence for solving optimization problems. It has the advantages of easy implementation, fast convergence, strong optimization ability and good robustness. However, the performance of DE is very sensitive to the design of different operators and the setting of control parameters. To solve these key problems, this paper proposes an improved self-adaptive differential evolution algorithm with a shuffled frog-leaping strategy (SFSADE). It innovatively incorporates the idea of the shuffled frog-leaping algorithm into DE, and at the same time, it cleverly introduces a new strategy of classification mutation, and also designs a new adaptive adjustment mechanism for control parameters. In addition, we have carried out a large number of simulation experiments on the 25 benchmark functions of CEC 2005 and two nonparametric statistical tests to comprehensively evaluate the performance of SFSADE. Finally, the results of simulation experiments and nonparametric statistical tests show that SFSADE is very effective in improving DE, and significantly improves the overall diversity of the population in the process of dynamic evolution. Compared with other advanced DE variants, its global search speed and optimization performance also has strong competitiveness.

A decay-based account of learning and adaptation in complex skills.

How do humans adapt to parametric changes in a task without having to learn a new skill from scratch? Many studies of memory and sensorimotor adaptation have proposed theories that incorporate a decay on prior events, which leads the agent to eventually forget old experiences. This study investigates if a similar decay mechanism can account for human adaptation in complex skills that require the simultaneous integration of cognitive, motor, and perceptual processes. In 2 experiments, subjects learned to play a novel racing video game while adapting to parametric changes in the physics of the game's controls. Human learning and performance were modeled using the ACT-R cognitive architecture, which has been used successfully to model learning and fluency across a wide range of skills in prior research. Anderson et al. (2019) introduced the Controller module, a new component of the architecture that learns the setting of control parameters for actions and allows the agent to execute the rapid and precise actions that are necessary for good performance on complex tasks. Model simulations support including a moderate time-based decay on the weight of the experiences that the Controller uses. This is implemented in the Controller module by discounting the influence of older observations which helps the agent to focus on recent experiences that better reflect the current relationship between different settings of a control parameter and the rate of payoff from using that setting. (PsycInfo Database Record (c) 2022 APA, all rights reserved).