Threshold Control Research Articles

Modeling and simulation of pure electric vehicle composite power supply

In order to solve the problem of high discharge current, serious heating and low service life of pure electric vehicle with single power battery system driving at high power. Design a top-level model of the composite power supply with battery and ultra-capacitor for pure electric vehicles in the simulation software ADVISOR, propose an energy management control strategy based on logic threshold control theory. Based on MATLAB/Simulink environment, set up a logic threshold limit filtering power shunt module and chose the working condition of NEDC for simulation. The results show that, compared to single battery system, a composite power system embedded in the logic threshold control strategy of filtering thought can distributes the power between the battery and the ultra-capacitor more reasonably, effectively improves the peak output power of the battery, avoiding high current charge and discharge of batteries, extends the life of the battery, and improve the vehicle's power and economy.

Dynamics of a delayed rumor spreading model with discontinuous threshold control

In this paper, we studied a delayed rumor spreading model with discontinuous threshold control. First, we studied the existence of equilibria of the subsystem. Regarding the delay as bifurcating parameter, the local asymptotic stability and Hopf bifurcation of the positive equilibrium are discussed by analyzing the corresponding characteristic equations of linearized systems. Then, we studied the existence of the sliding mode and analyzed the existence of the tangent equilibria, boundary equilibria, regular equilibria, and the stability of the pseudo-equilibrium. Finally, we provide some numerical simulations to verify the theoretical results.

The Study of Energy Management Scheme of Hybrid Energy Storage System for Responding High Demand with Long Period

Most large and controlled buildings require high power demand with long period. However, there are restrictions on building facilities and image when it comes to reducing electricity consumption. Another option is to install a roof-top solar power system, though due to the small installation area, it will only partially reduce energy use. Hybrid energy storage systems in combination with AGM batteries and LFP batteries are now offered as a lower cost energy management solution. AGM batteries are constrained in their ability to produce current, hence a larger current supply will reduce the battery's useable capacity. Therefore, in order to respond to high demand with long period, this research proposed a separate multi-level threshold control of the battery packs, and used the control factor in the form of current ratio to divide the load response for each battery type: On-Peak in AGM: LFP at 0.7:0.3, Off-Peak in AGM : LFP at 0.5 : 0.5. And when one battery was not ready for use, the other battery would be run at full capacity. The results showed that the AGM battery had a peak discharge current of 12.7A, or approximately 0.5C when coupled with an LFP battery, and when processing power storage system capacity, a loss of 8.99 kWh occurred. or 6.8% of the overall capacity.

Coordinated control and torque distribution of differential steering and anti-skid driving of distributed drive electric vehicle considering stability

Differential steering is a unique steering technology for distributed drive vehicles, which cannot only be applied to steering power, but also be used as a backup steering scheme for distributed drive vehicles. When the road adhesion conditions are poor, differential steering will lead to wheel slip, reduce the tire lateral force margin, and then affect the vehicle stability. To solve this issue, it is necessary to integrate the differential steering and anti-skid drive control. In this paper, the four-wheel distributed drive electric vehicle (DDEV), tire and wheel dynamics models are firstly established. Then the coordinated control strategy is proposed for anti-skid driving and differential steering of DDEV, where a neural network is adopted for the weights regulation of the two controllers. Next, an improved slip rate estimation method avoiding the calculation of vehicle speed is proposed which can reduce the error when the wheel angular acceleration is less than zero. Then, the slip rate fuzzy threshold controller is designed to control the wheel slip rate, and the feed forward-feedback differential torque controller is designed to obtain the differential torque required in steering. Considering the vehicle stability, the torque distribution adopts the quadratic programing method and combines the constraints of wheel slip rate and tire load rate to optimize the distribution of each wheel’s driving torque. Finally, the joint simulation and hardware-in-the-loop (HIL) are carried out to verify the effectiveness of the proposed improved slip rate estimation method and control strategy. The results show that the calculation error is reduced to 5.13% with the improved slip rate estimation results, and the superiority of proposed control method is verified by both simulation and HIL test.

Purdue Hog Cooling Pad Experience

The demand for food is increasing with the growing population. The pork industry is currently thriving and will continue to prosper as long as sows continue to reproduce. However, the reproduction of pigs is currently facing the problem of heat stress. Environmental temperatures are reaching extremes, and lactating sows produce less milk for offspring when they are in hot weather. The main reason that they produce less is because of undernourishment. Sows eat less when hot, because when they do eat, it actually causes them to heat-up. The goal of the overall cooling pad effort is to reduce the heat stress on the sows by allowing them to eat more and improving their performance. Researchers have developed a cooling pad made of high-density polyethylene (HDPE), copper piping, and an aluminum plate. These materials allow for easy waste heat transfer away from the sow. Cool water is pumped through the piping. When the hot sow lays on the pad, the water begins to warm and is eventually flushed with cool water. Various coolant flow patterns were tried in order to find the most effective way to use the pad, either by time interval or temperature threshold control. The primary goal of the summer research was aiding in the development of a cooling pad in resolving the problem of heat stress in sows and improving the manufacturability of multiple units to aid in large scale testing of the device.

Global dynamics of a Filippov epidemic system with nonlinear thresholds

Due to the intermittency of prevention and control measures, the right-hand function of the dynamic model describing the spread of infectious disease is often induced to be nonsmooth, which can be formulated by employing Filippov systems and have been widely studied recently. Note that these systems only consider the number of infected individuals as the switching threshold. However, infectious disease control depends not only on the number of infected individuals, but also on the change rate of infected individuals. To address this, we propose a Filippov system with media-influenced nonlinear thresholds which integrate the change rate of infected individuals into the threshold strategy. The main results reveal that the proposed threshold strategy can induce the complex dynamic behavior, including multiple sliding segments and pseudo-equilibrium. Furthermore, our main results show that variations of threshold (ET) and the nonlinear threshold control strategy not only lead to rich dynamics including bistability, but also have important implications for disease control.

Global dynamics analysis of a Filippov Hindmarsh–Rose neuron model

The electromagnetic induction induced by neuron membrane potential plays an important role in regulating its electrical activities, bistability and hidden dynamics. In this paper, a nonsmooth threshold control strategy is proposed, in which the membrane potential is utilized as the threshold to determine the switching function corresponding to the electromagnetic induction intensity and external stimulus current. Consequently, a four-dimensional Filippov-type HR neuron model is established. First, the existence, stability and global bifurcation behaviors of equilibrium points of two subsystems are discussed by using stability theory and numerical simulation. Then, the bistable behavior and evolution modes of subsystems are investigated based on the two-parameter bifurcation analysis. Further, the existence of various equilibrium points and sliding dynamics of the system are analyzed by Filippov convex method and Utkin’s equivalent control method. Finally, the sliding firing modes and multistable features under the control of the threshold are revealed by the method of fast-slow variable dissection. These results will provide useful theoretical support for understanding the hidden dynamic mechanism of neurons and constructing functional neural networks.

An enhanced hypotrochoid spiral optimization algorithm based intertwined optimal sizing and control strategy of a hybrid electric air-ground vehicle

Hybrid electric air-ground vehicles (HEAGVs) are deemed as promising transportations due to their great versatility, mobility, and environmental values. Capable of ground-driving, vertical-take-off-and-land, and near-ground flight, HEAGVs are competent to pass poor road conditions such as broken bridges or cliffs. As a crucial part of the HEAGV development, size of the power sources is hard to design owing to different characteristics of air-driving and ground-driving. Large power sources would result in increased fuel consumption and accelerating battery degradation, while small ones might lead to insufficient power supply. Thus, optimal sizing of the power sources is needed at the functional level of a HEAGV design. Moreover, at the performance level, the control strategy should be taken into account simultaneously, which significantly affects the sizing process. However, the intertwined optimal sizing and control strategy of HEAGV becomes difficult to address due to the expanded design space. Motivated by this, an efficient co-optimization strategy is presented for the studied HEAGV, intended to simultaneously find optimal sizing of the battery pack and turbine-generator pack and optimal logic threshold control parameters. The initial mass, fuel consumption, and battery degradation are chosen as optimization objectives to formulate an objective function. Then, a novel enhanced hypotrochoid spiral optimization algorithm (EHSOA) is proposed to address the intricated co-optimization problem. In this algorithm, an enhanced bi-considering mechanism is firstly proposed to avoid the optimization process being trapped in local optima. The co-optimization is implemented under an air-ground driving cycle. Results show that, compared to the initial design, the proposed strategy reduces initial mass, fuel consumption, and battery degradation by 5.08%, 26.10%, and 2.08%, respectively. Finally, the proposed EHSOA is demonstrated to be more qualified to solve the intricated co-optimization problem in comparison to other optimization algorithms. The proposed co-optimization strategy might provide theoretical insights for future HEAGV powertrain designs.

Global multistability and mechanisms of a memristive autapse-based Filippov Hindmash-Rose neuron model

Electromagnetic induction in the nervous system can be emulated by memristive autapses, which plays a critical role in regulating physiological functions. A discontinuous control strategy has been proposed by taking membrane potential as the threshold. Accordingly, a four-dimensional Filippov Hindmash-Rose (HR) neuron model is established by improving the memristive autapse with the threshold control strategy. The existence, stability, and bifurcation conditions of the two subsystems are discussed, and the bistable regions and their internal mechanism are further revealed with the help of two-parameter bifurcation analysis and global basins of attraction. Subsequently, the complex sliding mode dynamics of the model including sliding segments, various equilibrium points, and sliding bifurcations are analyzed via differential inclusions theory. Then, extensive numerical results exhibit that the proposed threshold strategy leads to the occurrence of sliding bursting, sliding limit cycle and coexisting attractors, and so on. Moreover, the mechanism of sliding electric activities, mode transition, and multistability under the threshold strategy feedback is revealed based on the fast-slow variable dissection method. Finally, the internal mechanism of multistable evolutionary patterns and stochastic bifurcations induced by Gaussian white noise is confirmed. The obtained results will contribute to the further design of functional neural networks and provide potential guidance for the treatment of patients with intellectual disabilities.

QoE-Aware Cross-Layer Adaptation for D2D Video Communication in Cooperative Cognitive Radio Networks

In this article, a cross-layer parameter optimization is considered for H.264 scalable video transmission in cognitive radio enabled device-to-device networks. Adaptive modulation and coding is considered at the physical layer along with video layer aware automatic repeat request (VLA-ARQ) at the link layer. The VLA-ARQ scheme is aware of video characteristics. Scalable video coding (SVC) based video streaming is carried out at the application layer. A novel cognitive radio network frame structure is proposed accommodating cooperative spectrum sensing (CSS), relaying primary user’s (PU) transmission, and secondary users’ (SU) opportunistic transmission to maximize quality of experience (QoE) and to minimize total power consumption. In SVC-based video streaming, successful reception of a particular video layer is dependent upon the successful reception of its higher layers, which influences the eventual QoE at the user end. A set of optimal values of CSS time duration, sensing decision threshold, cooperation, and transmission power control are obtained under the constraints of detection reliability, PU spectrum efficiency, and SU power budget. Extensive simulation results with respect to the system parameters show the efficacy of the proposed system. The numerical results demonstrate that the proposed technique offers more than 14% gain in overall utility with the proposed CSS and cross-layer parameter optimization strategy.

Adaptive 2-bits-triggered neural control for uncertain nonlinear multi-agent systems with full state constraints

This paper investigates an adaptive 2-bits-triggered neural control for a class of uncertain nonlinear multi-agent systems (MASs) with full state constraints. Considering the limitations of practical physical devices and operating conditions, MASs may suffer performance degradation or even crash while the system states are not restricted. With this in mind, combined with barrier Lyapunov function (BLF), an adaptive neural consensus control is developed to guarantee that the state constraints of all followers are not violated. Further, the conversion relationship between the state constraints of MASs and the synchronization error constraints is clarified more precisely, which could improve the synchronization performance of MASs. In addition, considering both trigger threshold setting and control signal transmission bits issues, a 2-bit trigger strategy is proposed to maximize the utilization of MASs bandwidth resources. Theoretical analysis shows that all signals are uniformly ultimately bounded. And the simulation results demonstrate its effectiveness.

Asynchronous numerical spiking neural P systems

Spiking neural (SN) P systems inspired from biological neural network are not only a kind of distributed and parallel membrane computing model, but also a new third-generation neural network model. It should be noted that SN P systems lack the ability to express information with data, however, numerical spiking neural (NSN) P systems can process information by using numerical variables as data structures. In this work, we investigate asynchronous numerical spiking neural (ANSN) P systems by combining with the knowledge of set theory and threshold control strategy. Moreover, the function of threshold (starting conditions of repartition protocol in NSN P systems) is replaced by the threshold set, that is, the production function can be executed if all of the involved variables are within the range of the threshold set. Under the control strategy of the threshold set and in the asynchronous mode, the computing power of NSN P systems is investigated. It is proved that the superfluous uncertainty introduced by ANSN P systems does not reduce the computing power, and ANSN P systems are still Turing universal as the number generating devices. Specifically, if the traditional threshold control strategy is maintained, the universality of asynchronous NSN P systems cannot be guaranteed, which can only characterize the semilinear set of natural numbers.

Gene-based whole genome sequencing meta-analysis of 250 circulating proteins in three isolated European populations

ObjectiveDeep sequencing offers unparalleled access to rare variants in human populations. Understanding their role in disease is a priority, yet prohibitive sequencing costs mean that many cohorts lack the sample size to discover these effects on their own. Meta-analysis of individual variant scores allows the combination of rare variants across cohorts and study of their aggregated effect at the gene level, boosting discovery power. However, the methods involved have largely not been field-tested. In this study, we aim to perform the first meta-analysis of gene-based rare variant aggregation optimal tests, applied to the human cardiometabolic proteome. MethodsHere, we carry out this analysis across MANOLIS, Pomak and ORCADES, three isolated European cohorts with whole-genome sequencing (total N = 4,422). We examine the genetic architecture of 250 proteomic traits of cardiometabolic relevance. We use a containerised pipeline to harmonise variant lists across cohorts and define four sets of qualifying variants. For every gene, we interrogate protein-damaging variants, exonic variants, exonic and regulatory variants, and regulatory only variants, using the CADD and Eigen scores to weigh variants according to their predicted functional consequence. We perform single-cohort rare variant analysis and meta-analyse variant scores using the SMMAT package. ResultsWe describe 5 rare variant pQTLs (RV-pQTL) which pass our stringent significance threshold (7.45 × 10−11) and quality control procedure. These were split between four cis signals for MARCO, TEK, MMP2 and MPO, and one trans association for GDF2 in the SERPINA11 gene. We show that the cis-MPO association, which was not detectable using the single-point data alone, is driven by 5 missense and frameshift variants. These include rs140636390 and rs119468010, which are specific to MANOLIS and ORCADES, respectively. We show how this kind of signal could improve the predictive accuracy of genetic factors in common complex disease such as stroke and cardiovascular disease. ConclusionsOur proof-of-concept study demonstrates the power of gene-based meta-analyses for discovering disease-relevant associations complementing common-variant signals by incorporating population-specific rare variation.

Design and characterization of a pneumatic micro glass beads matrix sensor for soil water potential threshold control in irrigation management

Soil moisture porous matrix sensors may be good alternatives to tensiometers for measuring soil–water matric potential (SMP) for irrigation scheduling based on soil–water status approaches. The objective of this paper is to present and evaluate a new porous matrix sensor (IGstat) for detecting specific SMP thresholds for possible application in irrigation scheduling regulated by the SMP threshold concept. The IGstat sensor uses a non-sintered, glass bead microspheres (microGB) core and an outer ceramic cup, having larger air bubbling pressure (BP), to establish hydraulic contact with the soil. Pneumatic, optical, or electrical properties of the microGB porous medium can be then measured to infer the SMP. This paper describes and evaluates the performance of IGstat sensors for SMP threshold detection, using the pneumatic mode with a small air flow applied and air pressure monitored in the sensor tubing. Five IGstat sensors were built with different microGB diameters (15–125 µm) having air BP varying from 6 to 40 kPa. A power function was fitted to the data, which can be used to select microGB diameters to build IGstat sensors of required air BP. The experimental setup proposed to determine the sensor BP by incremental air injection provided air BP values in good agreement with those observed in a soil evaporation experiment (average relative error of 7.6%). The sensor responses in soil, with a small air pressure applied to them, showed a sharp pressure decreases when the SMP approached the sensor air BP, decreasing to about zero for SMP equal to the sensor air BP. The proposed sensors and approach showed potential for irrigation scheduling.

Construction of Multiple Logic Circuits Based on Allosteric DNAzymes.

In DNA computing, the implementation of complex and stable logic operations in a universal system is a critical challenge. It is necessary to develop a system with complex logic functions based on a simple mechanism. Here, the strategy to control the secondary structure of assembled DNAzymes’ conserved domain is adopted to regulate the activity of DNAzymes and avoid the generation of four-way junctions, and makes it possible to implement basic logic gates and their cascade circuits in the same system. In addition, the purpose of threshold control achieved by the allosteric secondary structure implements a three-input DNA voter with one-vote veto function. The scalability of the system can be remarkably improved by adjusting the threshold to implement a DNA voter with 2n + 1 inputs. The proposed strategy provides a feasible idea for constructing more complex DNA circuits and a highly integrated computing system.

Research on accurate modeling and control for pneumatic electric braking system of commercial vehicle based on multi-dynamic parameters measurement

Accurate pressure control and fast dynamic response are vital to the pneumatic electric braking system (PEBS) for those commercial vehicles that require higher regulation precision of braking force on four wheels when braking force distribution is carried out under some conditions. Due to the lagging information acquisition, most feedback-based control algorithms are difficult to further improve the dynamic response of PEBS. Meanwhile, feedforward-based control algorithms like predictive control perform well in improving dynamic performance but because of the large amount of computation and complexity of this kind of control algorithm, it cannot be applied in real-time on the single-chip microcomputer, and it is still in the stage of theoretical research at present. To address this issue and for the sake of engineering reliability, this article presents a logic threshold control scheme combining analogous model predictive control (AMPC) and proportional control. In addition, an experimental device for real-time measuring PEBS multi-dynamic parameters is built. After correcting the key parameters, the precise model is determined and the influence of switching solenoid valve on its dynamic response characteristics is studied. For the control scheme, numerical and physical validation is executed to demonstrate the feasibility of the strategy and for the performance of the controller design. The experimental results show that the dynamic model of PEBS can accurately reflect its pressure characteristics. Furthermore, under different air source pressures, the designed controller can stably control the pressure output of PEBS and ensure that the error is within 0.08 bar. Compared with the traditional control algorithm, the rapidity is improved by 32.5%.

Incorporating the Filling–Spilling Feature of Depressions into Hydrologic Modeling

Surface depressions are one of the important impact factors of hydrologic processes and catchment responses. However, in many hydrologic models, the influence of depressions is often simulated in a lumped manner, which results in the insufficient characterization of the filling–spilling–merging–splitting dynamics of depressions and the threshold behavior of the overland flow. The objective of the research reported in this paper is to improve the simulation of depression-influenced hydrologic processes by capturing the threshold control of depressions. To achieve this objective, a Depression-oriented Soil and Water Assessment Tool (SWAT-D) is developed. Specifically, the intrinsic changing patterns of contributing area and depression storage are first determined and further incorporated into the SWAT to simulate the filling–spilling of depressions and depression-influenced overland flow dynamics. The SWAT-D was applied to a depression-dominated watershed in the Prairie Pothole Region to evaluate its performance and capability. The simulated and observed hydrographs at the watershed outlet showed good agreement, with only a 7% deviation between the simulated and observed volumes of discharges in 2004. The NSE values for the simulated monthly average discharges during calibration and validation periods were 0.78 and 0.71, respectively, indicating the ability of the SWAT-D in reproducing the depression-influenced catchment responses. In addition, the SWAT-D was compared with other depression-oriented modeling techniques (i.e., the lumped depression approach and probability distribution models), and the comparisons emphasized the improvement of the SWAT-D and the importance of the research reported in this paper.

Two‐threshold control limit policy in condition‐based maintenance

AbstractIn this paper, we consider a component exhibiting a continuous monotone degradation modeled as a Gamma process. The maintenance policy is of condition‐based type with periodic inspections. It follows the two threshold control limit policy with a preventive maintenance limit, a switching limit, and a given failure limit. Our interest is to minimize the annual expected maintenance cost subject to a constraint on the average availability. The decision variables are the preventive limit, the switching limit, and the inspections schedule. We derive exact results for the key performance indicators, but since they are numerically involved, we propose a simple, that is, involving necessary spreadsheet calculations, accurate approximation. We apply our model to a case from the process industry, namely, a carbon steel hydrogen dryer consisting of a cylindrical pressure vessel. This vessel suffers from corrosion with thickness growth modeled as a Gamma process. The optimization results show that although the annual maintenance cost increases with the mean and the coefficient of variation of the yearly corrosion thickness, its mean has a higher impact on cost. Based on the cost sensitivity analysis, we find that the impact of a vast increase of the corrective maintenance cost for the same inspection and preventive costs can be mitigated by appropriately reducing the corrective maintenance probability. Finally, we empirically show that the two threshold policy achieves an annual cost savings up to compared to the single threshold policy.

A novel hybrid energy management strategy of a diesel-electric hybrid ship based on dynamic programing and model predictive control

The energy-saving characteristics of diesel-electric series hybrid ships largely depend on their energy management strategy. In this paper, a strategy that combines dynamic programing and model predictive control (DP-MPC) is proposed to solve the energy management problems of diesel-electric hybrid ships. The DP-MPC strategy has considered some typical working conditions of a ship, and the corresponding influence of white noise disturbance on the control strategy was studied. The simulation results show that the DP-MPC strategy has an excellent anti-interference capability. The control performance of the DP-MPC strategy is then further analyzed and compared with the rule-based logic threshold control strategy. The simulation results show that the proposed DP-MPC strategy can save 2.5% of the fuel consumption and has a better anti-interference capability than the rule-based control strategy.

Heuristic Algorithm for Cross‐Platform Credit Risk Transmission Based on Hybrid Strategies

Credit risk transmission between cross‐platforms is an important issue in the construction of a credit service system. The effect of credit risk transmission between credit entities (nodes) is analyzed in this paper. A heuristic algorithm based on hybrid strategies (HAHS) is proposed to find risk transmission paths and calculate the influence of nodes. Besides, a novel community model is applied to predict the credit risk areas in advance. In detail, the mathematical association structure between credit entities is firstly given in the algorithm, and the breadth first search algorithm is used to find the hierarchical nodes on the credit risk transmission paths. Then, the characteristics of credit risk transmission are analyzed, and the calculation methods of single‐path and multipath influence are proposed. Finally, the credit entities are divided into communities based on a greedy strategy considering the characteristics of the credit entity association structure. The threshold control strategy is adopted to find global key nodes among all of the entities and local key nodes in communities, respectively, so as to realize the early warning of credit risk.