Parameter Control Method Research Articles

Dynamic game study of multi-channel supply chain under cap-and-trade regulation

This work focuses on the dynamic complexity study of a multi-channel supply chain composed of a manufacturer, an e-retailer, and a traditional retailer under cap-and-trade regulation. A long-term dynamic Stackelberg game model is studied in three situations: without cap-and-trade regulation, grandfathering approach, and benchmarking approach. This model is further discussed by means of the stable region diagram, 2D and 3D bifurcation diagrams, and the largest Lyapunov exponent diagram. The profit, carbon emission, and social welfare of the supply chains under the three different situations are compared. The results suggest that adjustments to order quantity, consumers' channel preference, and low-carbon preference should be kept within a suitable range. The benchmarking approach is more effective than the grandfathering approach in encouraging manufacturers to pay more attention to low-carbon products. From a societal point of view, the benchmarking approach can help the whole supply chain obtain more total profit and more total social welfare, and it can lower total carbon emissions. The parameter control method is further used to control the chaos in the system.

A variable parameter control method for charging pile load aggregator to participate in the fast frequency market

Abstract With the access of large-scale new energy sources, fast frequency regulation resources of the power system are scarce. A variable parameter control method for charging pile load aggregator to participate in the fast frequency market is proposed in this paper. It is that charging pile load aggregator actively adjusts the active power according to the frequency of the local power system, and the response speed is within 0-2 seconds. In addition, the variable parameter control method proposed in this paper specifically controls the power of charging pile according to the speed and amplitude of frequency drop, which has strong pertinence and simple control scheme, and further improves the frequency stability of the power grid.

A GCM Neural Network with Piecewise Logistic Chaotic Map

In order to explore dynamic mechanisms and chaos control of globally coupled map (GCM) chaotic neural networks, a new GCM model, called the PL-GCM model is proposed, of which a piecewise logistic chaotic map is used instead of a logistic map. As a result of the strong chaotic features of the map, the neurons’ period and chaotic characteristics over a wide range of parameters are discussed, the dynamic mechanism is demonstrated in detail, and the numerical simulations such as state evolution, the largest Lyapunov exponent (LLE), contour map, and so on are exhibited. Furthermore, chaos control of the proposed PL-GCM model is investigated by adopting two chaos control methods. It is shown that the network with conventional coupling or delay coupling can be precisely controlled to any specified periodic orbit by feedback control, and its dynamic associative memory is realized by the variable threshold parameter control method with external information. The results of simulations and experiments suggest that the network is controlled successfully and can output period patterns with a specified period that contains the stored pattern closest to the initial pattern. All features suggest that the network is fit for pattern recognition and information processing.

Research for axial power distribution control of a space nuclear reactor based on nonlinear model

During the operating process of a space nuclear reactor, once the local overload of heat energy occurs in the core, the corresponding fuel elements would be damaged and the lifespan of the reactor would be seriously reduced. In fact, the heat flux at any point in the core is proportional to the neutron flux density. A sufficiently flat axial neutron flux density distribution can protect the fuel assembly from frequent load changes. It is so clear that the axial power distribution control is crucial for the safe and stable operation of the space nuclear reactor. However, the space nuclear reactor is a distributed parameter system, which brings great challenges to the traditional lumped parameter control methods. In this paper, a distributed parameter control method is proposed to suppress the initial neutron flux peaks and realize the exponential stability of the neutron flux distribution control system. First of all, the spatiotemporal model of the reactor core based on the neutron diffusion equation is established. Then, the stability analysis of the control law for the neutron flux distributed parameter control problem is performed by constructing the Lyapunov function. The optimization technique based on the spatial differential linear matrix inequality is used to approximate the solution of the suboptimal integral control law. The control law is implemented by rotating drum, and the flat distribution of the axial power of the core is realized by the distributed parameter control. Finally, the numerical results are provided to validate the feasibility of the proposed control scheme.

All-position welding control system based on machine vision and nonlinear regression

Aiming at the problems of poor welding quality and low degree of automatic welding on the engineering site, a welding process parameter control method based on machine vision and nonlinear regression technology is proposed. Firstly, a vision unit and a peripheral sensor unit are designed to obtain the information of each influencing factor of the welding process parameters. Secondly, a clustering algorithm is used to improve the extraction accuracy of feature point coordinates of weld images. Thirdly, a nonlinear regression fitting method is proposed to determine the mathematical relationship between welding quality at different welding positions and corresponding process parameters. Experimental results show that the control system is easy to operate, and the flexible control of welding process parameters in the whole process is realized. The weld cumulative height and width deviations are less than 0.5 and 0.3 mm, respectively. The welding surface is stable and meets welding requirements. Therefore, this method is of great practical significance in engineering field welding.

Federalism, but how? The impact of vertical fiscal imbalance on economic growth. Evidence from Belgium*

AbstractThis paper applies both parametric and nonparametric synthetic control methods in the case of a specific federal reform. It evaluates the impact of Belgium's constitutional federalism reform of 1993 on GDP per capita. The findings show a negative impact of the reform on economic growth. This negative impact ceases soon after 2001, when a subsequent reform reduced the vertical fiscal imbalance that characterized the 1993 federal reform. It is argued that the presence of a strong federalism with subnational spending autonomy and, conversely, a centralized tax system, enhanced pork barrel spending, a common pool problem, thus damaging economic growth.

Study on complex dynamics for the waste electrical and electronic equipment recycling activities oligarchs closed-loop supply chain.

Nowadays, with more and more WEEE (waste electrical and electronic equipment) being abandoned, WEEE recycling activities are increasingly popular. In this paper, we build a closed-loop supply chain model and focus on the recycling behaviors of the members in this supply chain, which contains two manufacturers, the retailer, and the consumer. In the reverse chain, we set up dual channels and design two recycling methods: sell-back method and trade-in method. We use classical backward induction to run the model. And then we analyze the stability of the system and the impacts of some essential parameters by numerical simulation. The speed of the manufacture's decision adjustment has a significant effect on the stability of the model. Manufacturers should adopt a stable rather than radical price adjustment strategy in order to achieve stable profit growth in the whole supply chain and avoid a chaotic price war. Manufacturers should increase innovation investment in recycling technology to reduce the cost of remanufacturing products and improve the initiative in market competition. In the end, we adopt the parameter control method and the decision-making method to control chaos, and they both have a good control effect.

A Novel Surface Parametric Method and Its Application to Aerodynamic Design Optimization of Axial Compressors

A novel parametric control method for the compressor blade, the full-blade surface parametric method, is proposed in this paper. Compared with the traditional parametric method, the method has good surface smoothness and construction convenience while maintaining low-dimensional characteristics, and compared with the semi-blade surface parametric method, the proposed method has a larger degree of geometric deformation freedom and can account for changes in both the suction surface and pressure surface. Compared with the semi-blade surface parametric method, the method only has four more control parameters for each blade, so it does not significantly increase the optimization time. The effectiveness of this novel parametric control method has been verified in the aerodynamic optimization field of compressors by an optimization case of Stage35 (a single-stage transonic axial compressor) under multi-operating conditions. The optimization case has brought the following results: the adiabatic efficiency of the optimized blade at design speed is 1.4% higher than that of the original one and the surge margin 2.9% higher, while at off-design speed, the adiabatic efficiency is improved by 0.6% and the surge margin by 1.3%.

Research on Reliability of Information Push Robot Based on Image Self-learning

In order to improve the intelligent control ability of information push robot, an output reliability control method of information push robot based on image self-learning is proposed. The parameter identification model of robot output reliability positioning is constructed, and the pose correction and adaptive adjustment of information push robot are carried out by combining fuzzy constraint parameter control method. Adaptive learning control law is adopted to carry out adaptive feedback correction of robot spatial distribution parameter error under computer image vision, and computer image vision information tracking fusion method is adopted to realize adaptive positioning and fuzzy control of information push robot, and improve the ability of pose correction and error feedback adjustment. The simulation results show that the output reliability control accuracy of the information push robot is high and the adaptive control ability is good.

Robust registration algorithm based on rational quadratic kernel for point sets with outliers and noise

This paper proposes a new rigid registration algorithm based on the rational quadratic kernel to align point sets with outliers and noise. First of all, the multi-source point sets may contain a lot of outliers and noise and the traditional registration algorithm cannot handle the outliers and noise efficiently, this paper introduces the rational quadratic kernel to the rigid registration problem, which can resist outliers and suppress noise to improve the registration accuracy. Secondly, based on the new registration model, we present an iterative closest point (ICP) algorithm and use Lagrange multiplier and the singular value decomposition (SVD) to compute the rigid transformation. Moreover, the effect of the parameter is discussed detailly and a useful parameter control method is introduced to increase the accuracy and robustness of registration. A series of experiments on simulations and real data demonstrate that the proposed algorithm is more precise and robust than other algorithms.

A novel automated louver with parametrically-angled reflective slats; design evaluation for better practicality and daylighting uniformity

Daylighting harvest systems have been improved in recent decades. They have been dramatically used in green buildings to achieve a more sustainable living environment. However, their cost, sizes, shapes, materials, accuracy and control methods are considered as critical aspects towards their feasibility and practicality. The optimal integration between these aspects can achieve more reliable and practical daylight harvest systems. Grasshopper, as a parametric control method, can be exploited in such systems to optimise the use of each aspect. Parametric approach is considered as the most convenient way to control and manipulate different parameters simultaneously, with respond to the sun movement. This study investigates a parametrically controlled daylighting louver system, which can respond to the sun movement to collect as much daylight as possible inside the deep room, via using parametrically-angled reflective slats. To consider our main purpose of feasibility and performance, the study will evaluate the performance of the proposed system by changing its slats' size and shape to achieve optimum balance between practicality and performance. The study found that reducing the louver size and modifying the slat's curvature, can significantly increase the daylight coverage percentage inside the deep room from 93% to 98% within the illuminance standard range 300–500 lx, in addition to reducing the glare probability to an imperceptible level, besides achieving lighter weight and better strength-limber shape.

The Effect of Icing on the Output Parameters of the Airflow-Angle Sensor in Case of Failure of the Electric Heating System

At present, with the intensive operation of aircrafts in extremely harsh air conditions, their all-weather use, the work related to improving the flight safety is especially relevant. One of the directions of work in this topic is the study of the types of parametric failures of onboard equipment associated with the influence of the external environment in order to assess their impact on interacting systems and develop recommendations for identifying and countering detected failures. The presented paper considers the results of a full-scale experiment to determine the possibility of operation of the aerodynamic angle sensor in artificially formed icing conditions in case of failure of its electric heating system, as well as a description and results of subsequent modeling of the sensor icing process, and an assessment of its effect on the accuracy characteristics of the sensor output signal. The statement of the research problem is formulated as follows: it is necessary to ensure the testing of the sensor and the subsequent processing of the results obtained in order to form a generalized mathematical model of the sensor icing process, as well as to solve the problem of introducing additional errors into the sensor signals, which makes it possible to study the behavior of air signal systems using vane sensors of aerodynamic angles with artificially introduced failures. A full-scale experiment was carried out in an air-cooling tube on the basis of the enterprise JSC "UKBP". Numerical modeling and determination of correction factors were carried out in the software packages Ansys FENSAP ICE and MathCAD. Analysis of the results achieved in the course of modeling confirmed the convergence of the model obtained with the data of real experiments in an air-cooling tube. The data obtained made it possible to confirm the possibility of detecting distorted values for the angle of attack by the parametric quorum control method.

Dynamic Response of an Inverted Pendulum System in Water under Parametric Excitations for Energy Harvesting: A Conceptual Approach

In this paper, we have investigated the dynamic response, vibration control technique, and upright stability of an inverted pendulum system in an underwater environment in view point of a conceptual future wave energy harvesting system. The pendulum system is subjected to a parametrically excited input (used as a water wave) at its pivot point in the vertical direction for stabilization purposes. For the first time, a mathematical model for investigating the underwater dynamic response of an inverted pendulum system has been developed, considering the effect of hydrodynamic forces (like the drag force and the buoyancy force) acting on the system. The mathematical model of the system has been derived by applying the standard Lagrange equation. To obtain the approximate solution of the system, the averaging technique has been utilized. An open loop parametric excitation technique has been applied to stabilize the pendulum system at its upright unstable equilibrium position. Both (like the lower and the upper) stability borders have been shown for the responses of both pendulum systems in vacuum and water (viscously damped). Furthermore, stability regions for both cases are clearly drawn and analyzed. The results are illustrated through numerical simulations. Numerical simulation results concluded that: (i) The application of the parametric excitation control method in this article successfully stabilizes the newly developed system model in an underwater environment, (ii) there is a significant increase in the excitation amplitude in the stability region for the system in water versus in vacuum, and (iii) the stability region for the system in vacuum is wider than that in water.

Axial power distribution control in fast nuclear reactor based on proportional spatial-derivative method

Axial power distribution control is one of the key issues for Nuclear Power Plant (NPP) operation. In the most of NPPs, the axial power distribution control is realized by the Lumped Parameters Control (LPC) method, which often takes the axial power difference between adjacent nodes as the controlled object. However, the nuclear reactor core has a typical distributed parameters feature. With the number of discrete nodes increases, the control system design is more and more complex. Therefore, it is necessary to develop the Distributed Parameter Control (DPC) method. In this work, a DPC method based on the Proportional Spatial-Derivative (PSD) controller is presented. Experiments show that the power distribution control system based on the PSD control method can inhibit the power peak. Meanwhile, the comparison between the PSD and PID controllers shows that the DPC system based on the PSD method has better dynamic performance than LPC system.

The Effectiveness of School Closures and Other Pre-Lockdown COVID-19 Mitigation Strategies in Argentina, Italy, and South Korea

The rapid spread of COVID-19 forced policy-makers to swiftly find solutions to reduce infection rates and keep mortality as low as possible. Empirical analyses on the effectiveness of control measures are hereby of primary importance. School closures were among the earliest measures enacted by the governments of most countries. However, while schools are now reopening in many countries, the impact of school closures on the course of the epidemic is still an open question. Adopting parametric and non-parametric synthetic control methods we estimate the effectiveness of pro-active school closures, and other early social distancing interventions, in three countries that reacted relatively early during the course of the pandemic. Our findings suggest that these interventions were effective at reducing the mortality rate of COVID-19, especially when enacted early.

Investigation of parametric control method and model in abrasive belt grinding of nickel-based superalloy blade

Abrasive belt grinding is gradually recognized as an effective machining technology for blade. However, the complex contact characteristics of this technology make the grinding quality of blade unable to be precisely controlled. In order to solve this problem, a parametric control method for precision abrasive belt grinding of blade was proposed, and a multi-parameter test platform for state parameters (grinding force, vibration, and temperature) was established. The grinding experiments of nickel-based superalloy samples were carried out, and the prediction model of state parameters based on back propagation neural network was constituted. The prediction accuracy of the model was 93.58%. On this basis, the grinding experiments of nickel-based superalloy blade were conducted. The influence of state parameters on the evaluation parameters (material removal, profile accuracy, surface quality) was analyzed and the optimal grinding parameters were determined. The experimental results showed that the machining quality of new-type aeroengine blades can be effectively improved by this parametric control method and model.

Exploring the robustness of public transportation for sustainable cities: A double-layered network perspective

The reliable and safe operation of an urban public transportation (UPT) system is of great significance to the sustainable development of a city. In-depth analyses of UPT network by exploring the topological structure and passenger travel features can help explain the mechanisms driving reliable UPT operations. Based on complex network theory, we proposed a robust model for a Bus-Subway double-layered network (B–S DLN) from the structural and functional perspectives. The following indicators were used to quantify the structural robustness of B–S DLN: average path length (APL), relative size of giant component (RSGC) and global network efficiency (GNE). The functional robustness was measured using the ratio of passenger flow loss (R). Using urban traffic data from the Xicheng District of Beijing, we analyzed the cascading failure of B–S DLN based on a nonlinear load-capacity model with two capacity control parameters: α and β. The simulation results show that α and β significantly impact the cascading failure process of B–S DLN. An increase in β or decrease in α can enhance the network’s ability to resist cascading failure. The parameter control method revealed that the B–S DLN achieves strong robustness when α = 0.2 and β = 0.5. Comparing the network robustness under a random attack and intentional attack, the B–S DLN is more robust under an intentional attack than in the random attack mode. This indicates that the robustness of B–S DLN can be significantly improved by rationally increasing station capacities.

A Self-Adaptive Parameter Control Method Based on Entropy and Security Market Line for Scheduling Problems

Scheduling problems, as one of the classic combinatorial optimization problems are essential issues in many fields. Various meta-heuristic algorithms have been adopted to solve scheduling problems. However, parameter control problem is still crucial to the performance of algorithms. In this paper, we propose a self-adaptive parameter control method based on entropy and security market line, fully considering the characteristics scheduling problems. It consists of two key parts: locus-entropy strategy and parameter-control strategy. Firstly, the entropy on each genetic locus is calculated to accurately evaluate the population status of scheduling algorithms. Then, a parameter-control strategy based on the conception of security market line is proposed to address the issue that the nature of multipeak in scheduling problems makes algorithms fall into local optimal solutions. The strategy maintains the solutions of good quality and eliminate the solutions of poor quality by using locus-entropy as feedback. Through our method, the balance between exploitation and exploration is kept in algorithms to perform well in scheduling problems with different dimensions and characteristics. These strategies are tightly linked to adjust parameters adaptively without introducing new parameters, so that meta-heuristic algorithms equipped with the proposed approach are able to find a better solution. Moreover, our parameter control method is universal for meta-heuristic algorithms. The proposed approach is hybrid with genetic algorithm and particle swarm optimization. The hybrid algorithms are first compared with the standard algorithms in multipeak benchmark functions, then with other variants of the standard algorithms in real-world single and multi-objective scheduling problems. The results demonstrate that the proposed approach is valid for different kinds of algorithms to enhance the performance of solving a variety of scheduling problems.

Online Estimation and Control for Feed Drive Systems With Unmeasurable Parameter Variations

This paper develops an online parameter estimation and control method for both rigid and flexible feed drive systems with unmeasurable parameter variations. The perturbations of the state-space model caused by the parameter variations are formulated, thereby making it possible to obtain the parameter variations in real-time by estimating the perturbations. To estimate the perturbations, they are regarded as the extended states and estimated through the extended-state-observer. With the estimation method, a novel state feedback control structure with double integrators is further proposed, which can eliminate the steady-state tracking error at constant velocities. The H$_\infty $ optimization technique is used to design the proposed state feedback controller. A simulation is conducted that integrates the estimator and the controller for a ball screw setup with mass-dependent resonant modes, where several proposed state feedback controllers are linearly interpolated into a gain-scheduling controller and scheduled by the estimated mass. The results demonstrate that the designed gain-scheduling state feedback controller outperforms a linear-time-invariant state feedback controller and an adaptive backstepping sliding mode controller. The proposed method is experimentally validated on a rigid linear-motor-driven motion stage, of which the results indicate the proposed estimation method can accurately estimate the parameter variations.

Qualities, Challenges and Future of Genetic Algorithms

Genetic algorithms, computer programs that simulate natural evolution, are increasingly applied across many disciplines. They have been used to solve various optimisation problems from neural network architecture search to strategic games, and to model phenomena of adaptation and learning. Expertise on the qualities and drawbacks of this technique is largely scattered across the literature or former, motivating an compilation of this knowledge at the light of the most recent developments of the field. In this review, we present genetic algorithms, their qualities, limitations and challenges, as well as some future development perspectives. Genetic algorithms are capable of exploring large and complex spaces of possible solutions, to quickly locate promising elements, and provide an adequate modelling tool to describe evolutionary systems, from games to economies. They however suffer from high computation costs, difficult parameter configuration, and crucial representation of the solutions. Recent developments such as GPU, parallel and quantum computing, conception of powerful parameter control methods, and novel approaches in representation strategies, may be keys to overcome those limitations. This compiling review aims at informing practitioners and newcomers in the field alike in their genetic algorithm research, and at outlining promising avenues for future research. It highlights the potential for interdisciplinary research associating genetic algorithms to pulse original discoveries in social sciences, open ended evolution, artificial life and AI.