Accurate System Model Research Articles

Compact Analysis of the Necessity of Padé Approximation for Delayed Continuous-time Models in LQR, H-Infinity and Root Locus Control Strategies

This paper presents a comprehensive analysis of the need for the Padé approximation for continuous-time models with delays, focusing on its critical role in addressing the control challenges posed by time delays. Time delays, often referred to as dead times, transport delays or time lags, are inherent in a wide range of industrial and engineering processes. These delays introduce phase shifts that degrade control performance by reducing control bandwidth and threatening the stability of closed-loop systems. Accurate modelling and compensation of these delays is essential to maintain system stability and ensure effective control. This paper highlights the difficulties that arise when using advanced control techniques such as root locus (RL), linear quadratic regulator (LQR) and H-infinity (H_∞) control in systems with delays. Representing delays in exponential form leads to an infinite number of state problems, complicating the design and analysis of controllers in such systems. To address these challenges, the Padé approximation is proposed as an effective method for approximating time delays with rational polynomials of appropriate order. This approach allows for more accurate simulation, system analysis and controller design, thereby mitigating the problems caused by delays. The paper also provides a detailed comparative analysis between the Padé approximation and Taylor polynomials, demonstrating the superiority of the former in achieving accurate delay modelling and control performance. The results show that the use of Padé approximation not only improves the accuracy of system models, but also improves the robustness and stability of control strategies such as RL, LQR, and H_∞. These results highlight the importance of the Padé approximation as a valuable tool in the design of delay-affected control systems, offering significant advantages for both theoretical and practical applications.

Multiple lubricated joints with long and short bearings in multibody mechanical systems - Modeling, simulation, and performance analysis

This study examines the effect of multiple lubricated imperfect long and short bearings on the performance of a multibody mechanical system. Unlike the typical assumption of a single perfect or imperfect lubricated joint due to modeling difficulties, this research considers the practical impacts of clearances in multiple joints. While unlubricated joints generally cause significant performance issues, lubricants reduce these effects, creating more localized peaks. The findings show that as the number of lubricated joints increases, both the magnitude and frequency of these peaks rise. In systems with multiple journal bearings, torque peaks become more noticeable due to the additional degrees of freedom introduced by the clearances. These degrees of freedom amplify acceleration, leading to higher lubricant reaction forces, which in turn require greater motor torque peaks to maintain the system's kinematics. Shorter lubricated joints exhibit more severe peaks than longer ones, mainly due to side leakage causing axial pressure variation and reduced damping capacity. The study highlights the need to replace idealized joints with imperfect ones for more accurate modeling of practical systems.

Adaptive robust motion control for hydraulic load sensitive systems considering displacement dynamic compensation

Hydraulic load-sensitive systems (HLSS) are widely used for high power density and energy efficiency. This study introduces an adaptive, energy-efficient HLSS with a valve-controlled variable motor. The system faces challenges from non-linearities, including internal higher-order dynamics due to displacement changes and external unknown disturbances, which hinder precision applications. To address this issue, this study explores HLSS principles to develop an accurate system model. Subsequently, an adaptive robust motion control that considers displacement compensation (DCARC) is proposed using the established model. DCARC can learn unknown parameters online and compensate the model more accurately to improve control accuracy. Experiments show that considering the higher order dynamic effects caused by displacement in the system can improve model accuracy and effectively reduce the burden of parameter adaptation and robust feedback terms. High-precision and energy-efficient HLSS motion is verified and realized in the study. The control accuracy of DCARC is 19.4% higher than that of conventional adaptive robust control (ARC). Under experimental conditions, the proposed system can improve energy efficiency by up to five times compared to valve-controlled fixed displacement motor systems (VFDS).

On the Foundations of Conflict-Driven Solving for Hybrid MKNF Knowledge Bases

Abstract Hybrid MKNF Knowledge Bases (HMKNF-KBs) constitute a formalism for tightly integrated reasoning over closed-world rules and open-world ontologies. This approach allows for accurate modeling of real-world systems, which often rely on both categorical and normative reasoning. Conflict-driven solving is the leading approach for computationally hard problems, such as satisfiability (SAT) and answer set programming (ASP), in which MKNF is rooted. This paper investigates the theoretical underpinnings required for a conflict-driven solver of HMKNF-KBs. The approach defines a set of completion and loop formulas, whose satisfaction characterizes MKNF models. This forms the basis for a set of nogoods, which in turn can be used as the backbone for a conflict-driven solver.

Analysis and control design for input‐series output‐parallel multi‐channel inductive power transfer system

AbstractTo realize high‐power inductive power transfer (IPT) for fast charging of electric vehicles (EVs), an input‐series output‐parallel (ISOP) multi‐channel IPT system is analysed in this paper, and an output control strategy based on single neuron controller is proposed to improve the stability of the system. Firstly, the steady‐state operating conditions of ISOP‐IPT system is analysed based on different compensation networks which shows that the constant‐voltage‐output compensation networks are more suitable for the proposed circuit structure. Then, to improve the voltage equalization between channels, an open‐loop control method combining parameter design and phase‐shift control is proposed against different coil misalignments. At the same time, based on the single neuron controller, the system output closed‐loop control is realized without the need of accurate system modelling. Finally, a three‐channel ISOP‐IPT experimental system was constructed, which operated stably at 2.9 kW with a power efficiency of 93.38%. The system closed‐loop control with control time of 22 ms is realized. The voltage equalization control offset is less than 1%.

Constitutive relationship of soil in contact with mortar under continuous loading

Mortars will remain critical in future land wars due to their flexibility and versatility. When mortars are fired continuously, the contact soil is gradually compacted by the mortar base plate, and dynamic research into this process is the basis for innovative mortar design. However, the discontinuity and nonlinearity of soil contact absolutely necessitate the constitutive relationship of soil contact, which is difficult to study. Therefore, this study conducted experimental research and theoretical derivation to establish an accurate dynamic model of the mortar system. First, based on the nonlinear elastic–plastic theory and the stress–strain relationship of soil under cyclic loading, a theoretical analysis method for the constitutive relationship of contact soil under continuous loading was proposed. Second, an experimental and testing system was designed to simulate launch loads, and the stress–strain response of soil under continuous impact loads was obtained experimentally. Subsequently, based on theoretical analysis and experimental data, the stress–strain relationship during the gradual compaction of soil was established using the least squares method. Finally, a constitutive relationship model of the contact soil in the mortar system was established in ABAQUS using the VUMAT subroutine interface, and the calculated results were compared and analyzed with traditional calculation results. The results indicated that studying the constitutive relationship of mortar in contact with soil during continuous firing using this method can improve the accuracy of dynamically modeling mortar systems. Moreover, this study has practical value in the engineering design of mortar systems.

Seasonal dynamics of chemistry in an Arctic glacier-fed river

Arctic rivers, intricately linked to fjord systems, wield significant influence over the geochemical and biological dynamics of the upper Arctic Ocean, providing it with freshwater, nutrients, suspended particles, and potentially harmful pollutants. To comprehend the full picture of the Arctic ecosystem, it is crucial to understand how these rivers vary across regions and seasons, especially considering ongoing climate changes. However, comprehensive studies that address long-term observations and seasonal variations in Arctic rivers' geochemical composition remain scarce. In this study, we present comprehensive long-term investigations of the seasonal variations in elemental concentrations in Bayelva, a high Arctic glacier-fed river. By analyzing 224 surface water samples, collected during different seasons between 2011 and 2020, we elucidate the diverse influences of marine, geological and atmospheric factors on the river chemistry. Our findings underscore the importance of marine-influenced snowmelt in the early flow season, which leads to elevated concentrations of marine and trace elements in the runoff water at the onset of melting, with concentrations subsequently decreasing as the snow melting continues. Glacial meltwater dominates the river chemistry during the peak flow season, during which elemental concentrations are at their lowest. Late flow season exhibits high elemental concentrations, primarily driven by weathering processes. Additionally, heavy rain and freezing events play a crucial role in sudden alterations in river chemistry. We highlight the dynamic response of Arctic river systems to such environmental drivers as key players in transporting essential micro- and macro-nutrients, as well as potentially harmful pollutants to adjacent fjord systems. Given their susceptibility to climate change, continuous monitoring is essential for understanding future changes. This study provides data for a better foundation for accurate climate modeling of biogeochemical systems in the Arctic environment.

Trust-Based Detection and Mitigation of Cyber Attacks in Distributed Cooperative Control of Islanded AC Microgrids

In this study, we address the challenge of detecting and mitigating cyber attacks in the distributed cooperative control of islanded AC microgrids, with a particular focus on detecting False Data Injection Attacks (FDIAs), a significant threat to the Smart Grid (SG). The SG integrates traditional power systems with communication networks, creating a complex system with numerous vulnerable links, making it a prime target for cyber attacks. These attacks can lead to the disclosure of private data, control network failures, and even blackouts. Unlike machine learning-based approaches that require extensive datasets and mathematical models dependent on accurate system modeling, our method is free from such dependencies. To enhance the microgrid’s resilience against these threats, we propose a resilient control algorithm by introducing a novel trustworthiness parameter into the traditional cooperative control algorithm. Our method evaluates the trustworthiness of distributed energy resources (DERs) based on their voltage measurements and exchanged information, using Kullback-Leibler (KL) divergence to dynamically adjust control actions. We validated our approach through simulations on both the IEEE-34 bus feeder system with eight DERs and a larger microgrid with twenty-two DERs. The results demonstrated a detection accuracy of around 100%, with millisecond range mitigation time, ensuring rapid system recovery. Additionally, our method improved system stability by up to almost 100% under attack scenarios, showcasing its effectiveness in promptly detecting attacks and maintaining system resilience. These findings highlight the potential of our approach to enhance the security and stability of microgrid systems in the face of cyber threats.

Homogenized Modeling of CO2/CO Reduction Catalyst Microenvironments with Intermixed Hydrophobic and Hydrophilic Regions

Electroreduction of CO and CO2 offer a pathway toward renewable-powered synthesis of hydrocarbon chemicals and fuels. Both the composition and the structure of cathodic catalyst microenvironments in gas diffusion electrodes play a dominant role in determining the product composition and efficiency metrics. Porous and hydrophilic copper environments are known to produce multi-carbon species and exhibit large active surface areas, but recent investigations have shown that the inclusion of sporadically-distributed porous hydrophobic regions creates additional pathways for the delivery of gaseous reactants, thereby mitigating transport limitations seen in pure Cu catalysts.As the design space for catalysts has grown to include a wide variety of material and geometric parameters, accurate PDE-based modeling and simulation of such systems has become necessary to understand and optimize the coupled reaction and transport processes that determine cell performance. High fidelity models, however, pose immense computational challenges. Physical and chemical processes occur over a wide variety of spatiotemporal scales, ranging from pore-scale reaction and transport to device-scale gradients in species concentrations. Additionally, the spatial orientation of these features necessitates modeling in multiple dimensions, and transport occurs simultaneously through two phases located within randomly-intermixed porous regions. As a result of all these factors, numerical simulation of high fidelity models is prohibitively expensive.In this work, we develop, numerically simulate, and validate a predictive model that efficiently incorporates a wide range of physical and chemical processes occurring in catalyst microenvironments with intermixed hydrophobic and hydrophilic regions. We employ homogenization as a means of model reduction, constructing a medium fidelity model that captures both microenvironment geometric scales and full device scales in 3D. Our model, coupled with the appropriate numerical methods for treatment of stiff and multi-dimensional problems, permits low-cost exploration of the high-dimensional parameter space associated with recently developed catalysts, offering quantitative insight into the optimal design of microenvironments for CO and CO2 electroreduction.This work is supported by the United States Department of Energy under grant DE-SC0021633.

Electronic Polarizability Tunes the Function of the Human Bestrophin 1 Cl- Channel.

Mechanisms of anion permeation within ion channels and nanopores remain poorly understood. Recent cryo-electron microscopy structures of the human bestrophin 1 Cl- channel (hBest1) provide an opportunity to evaluate ion interactions predicted by molecular dynamics (MD) simulations against experimental observations. Here, we implement the fully polarizable forcefield AMOEBA in MD simulations on different conformations of hBest1. This forcefield models multipole moments up to the quadrupole; therefore, it captures induced dipole and anion-π interactions. We show that key biophysical properties of the channel can only be simulated when electronic polarization is included in the molecular models and that Cl- permeation through the neck of the pore is achieved through hydrophobic solvation concomitant with partial ion dehydration. Furthermore, we demonstrate how such polarizable simulations can help determine the identity of ion-like densities within high-resolution cryo-EM structures and that neglecting polarization places Cl- at positions that do not correspond with their experimentally resolved location. Overall, our results demonstrate the importance of including electronic polarization in realistic and physically accurate models of biological systems, especially channels and pores that selectively permeate anions.

The Symplectic Adjoint Method: Memory-Efficient Backpropagation of Neural-Network-Based Differential Equations.

The combination of neural networks and numerical integration can provide highly accurate models of continuous-time dynamical systems and probabilistic distributions. However, if a neural network is used n times during numerical integration, the whole computation graph can be considered as a network n times deeper than the original. The backpropagation algorithm consumes memory in proportion to the number of uses times of the network size, causing practical difficulties. This is true even if a checkpointing scheme divides the computation graph into subgraphs. Alternatively, the adjoint method obtains a gradient by a numerical integration backward in time; although this method consumes memory only for single-network use, the computational cost of suppressing numerical errors is high. The symplectic adjoint method proposed in this study, an adjoint method solved by a symplectic integrator, obtains the exact gradient (up to rounding error) with memory proportional to the number of uses plus the network size. The theoretical analysis shows that it consumes much less memory than the naive backpropagation algorithm and checkpointing schemes. The experiments verify the theory, and they also demonstrate that the symplectic adjoint method is faster than the adjoint method and is more robust to rounding errors.

Investigation on the thermal characteristics of double-driven hydrostatic lead screw micro-feed system

The micro-feed system of the dual-drive hydrostatic lead screw (DDHLS) can overcome the accuracy limitations inherent in traditional hydrostatic lead screw through the process of differential synthesis, thereby achieving exceedingly low speeds and exceptional precision in feed operations. Nevertheless, due to the complex structural form of DDHLS, both heat generation and heat dissipation are different from conventional hydrostatic functional components. Therefore, for DDHLS micro-feed system, it is necessary to deeply investigate its thermal performance and evolution rules to improve its thermal stability. Based on the analysis of multi-field coupling, the accurate thermal model of the DDHLS micro-feed system is formulated. Taking into account the variable viscosity of the oil film and the heat dissipation conditions within the oil film region, the thermal behavior of DDHLS feed system is analyzed systematically. The accuracy of the thermal performance model of DDHLS feed system is verified by experiments. The findings indicate that the DDHLS micro-feed system has better thermal performance than the dual-axis differential feed system based on rolling components. Furthermore, optimizing the heat dissipation pathway of the DDHLS can significantly mitigate the thermal effects exerted by the hydrostatic nut assembly on the screw.

Digital Twin-Enhanced Control for Fuel Cell and Lithium-Ion Battery Hybrid Vehicles

With the development of lithium-ion batteries and fuel cells, the application of hybrid power systems is becoming more and more widespread. To better optimize the energy management problem of fuel cell hybrid systems, the accuracy of system modeling and simulation is very important. The hybrid system is formed by connecting the battery to the fuel cell through an active topology. Digital twin technology is applicable to the mapping of physical entities to each other with high interactivity and fast optimization iterations. In this paper, a relevant model based on mathematical logic is established by collecting actual operational data; subsequently, the accuracy of the model is verified by combining relevant operating conditions and simulating the model. Subsequently, a three-dimensional visualization model of a hybrid power system-based sightseeing vehicle and its operating environment was established using digital twin technology to improve the model simulation of the fuel cell hybrid power system. At low speeds, the simulation results of the hybrid power system-based sightseeing vehicle have a small error compared with the actual running state, and the accuracy of the data related to each internal subcomponent is high. In the simple interaction between the model display vehicle and the environment, the communication state can meet the basic requirements of the digital twin model because the amount of data to be transferred is small. This study makes a preliminary attempt at digital parallelism by combining mathematical logic with visualization models and can be used as a basis for the subsequent development of more mature digital twin models.

Development of data-driven modeling method for nonlinear coupling components

This research introduces a methodology for data-driven regression modeling of components exhibiting nonlinear characteristics, utilizing the sparse identification of nonlinear dynamics (SINDy) method. The SINDy method is extended to formulate regression models for interconnecting components with nonlinear traits, yielding governing equations with physically interpretable solutions. The proposed methodology focuses on extracting a model that balances accuracy and sparsity among various regression models. In this process, a comprehensive model was generated using linear term weights and an error histogram. The applicability of the proposed approach is demonstrated through a case study involving a sponge gasket with nonlinear characteristics. By contrasting the predictive model with experimental responses, the reliability of the methodology is verified. The results highlight that the regression model, based on the proposed technique, can effectively establish an accurate dynamical system model, accounting for realistic conditions.

Information System Model and Key Technologies of High-Definition Maps in Autonomous Driving Scenarios.

Background: High-definition maps can provide necessary prior data for autonomous driving, as well as the corresponding beyond-line-of-sight perception, verification and positioning, dynamic planning, and decision control. It is a necessary element to achieve L4/L5 unmanned driving at the current stage. However, currently, high-definition maps still have problems such as a large amount of data, a lot of data redundancy, and weak data correlation, which make autonomous driving fall into difficulties such as high data query difficulty and low timeliness. In order to optimize the data quality of high-definition maps, enhance the degree of data correlation, and ensure that they better assist vehicles in safe driving and efficient passage in the autonomous driving scenario, it is necessary to clarify the information system thinking of high-definition maps, propose a complete and accurate model, determine the content and functions of each level of the model, and continuously improve the information system model. Objective: The study aimed to put forward a complete and accurate high-definition map information system model and elaborate in detail the content and functions of each component in the data logic structure of the system model. Methods: Through research methods such as the modeling method and literature research method, we studied the high-definition map information system model in the autonomous driving scenario and explored the key technologies therein. Results: We put forward a four-layer integrated high-definition map information system model, elaborated in detail the content and functions of each component (map, road, vehicle, and user) in the data logic structure of the model, and also elaborated on the mechanism of the combined information of each level of the model to provide services in perception, positioning, decision making, and control for autonomous driving vehicles. This article also discussed two key technologies that can support autonomous driving vehicles to complete path planning, navigation decision making, and vehicle control in different autonomous driving scenarios. Conclusions: The four-layer integrated high-definition map information model proposed by this research institute has certain application feasibility and can provide references for the standardized production of high-definition maps, the unification of information interaction relationships, and the standardization of map data associations.

Analysis on battery thermal management system based on flat heat pipe at high discharging rate

Power batteries are the core power source for electric vehicles. However, battery thermal management technology for electric vehicles will face more severe challenges in the future. Flat heat pipe has gradually received much attention in the field of battery thermal management due to its high heat conduction coefficient and lightweight structure. This paper focuses on the influence of flat heat pipe structural parameters on battery thermo-electrical performance by establishing simulation model of the battery cooling system, which makes contribution to the flat heat pipe structure design on optimizing battery overall performance. Based on the mechanism of working medium flow effect of flat heat pipe on the battery electrochemical heat generation, a coupled model of flat heat pipe-based battery system is established according to the relationship between battery electrochemical heat generation performance and flat heat pipe heat transfer characteristics. The accuracy of system model is further verified by experiment, and the battery thermo-electrical characteristics under different discharging conditions is simulated. The flat heat pipe-based battery thermal management system can effectively reduce the battery maximum temperature while improving the uniformity of battery temperature and state of charge (SOC). The reduction of the vapor chamber thickness causes the uneven distribution of vapor thermal resistance, so the battery maximum temperature and maximum temperature difference will become larger, which will further cause the uneven distribution of battery internal resistance, and this will finally increase the non-uniformity of battery module discharging current distribution. A structural design method of the flat heat pipe-based battery thermal management system is developed with the battery thermo-electrical coupling performance as the design objective and the flat heat pipe structural parameters (total thickness, vapor chamber thickness, total length) as the optimization variables. Compared with the original scheme, the battery maximum temperature can be reduced by 6.4% under transient high-rate discharging conditions with the modified flat heat pipe based on the proposed design method, while the battery maximum temperature and SOC difference can also be reduced by 18.4% and 16.3%, respectively.

Perturbation estimation based single-loop decoupling control strategy for DC-based DFIG to augment MPPT and FRT capabilities

This paper proposes a single-loop decoupling controller with high-gain perturbation observer (HP-FLC) for double-VSC DFIG which can be connected to DC grid to extract maximum wind power and improve fault ride-through (FRT) capability. Lumped perturbation terms are defined in the HP-FLC to include all unknown, time-varying dynamic and external perturbations, which are actively estimated by high-gain perturbation observer. Then, a more time-efficient single-loop feedback decoupling controller is designed to compensate the perturbation estimation. This control strategy combines the advantages of classical feedback controller and perturbation observer, which is separated from the accurate system model and easy to implement. Simulation results show that the proposed control strategy can achieve better wind power extraction and stronger FRT capability compared with double-loop feedback linearization controller (DL-FLC) and single-loop feedback linearization controller (SL-FLC), and the transient performance is better than that of the sliding-mode feedback controller (SMC-FLC). Finally, an experimental platform is built to confirm the practical implementation of the proposed approach.

Decentralized periodic event‐triggered control for smart water distribution systems via digital twin approach

AbstractDecentralized periodic event‐triggered control can reduce the working time and data transmissions in the sensor nodes for feedback control loops, making it suitable for wireless networked control systems, especially those with large physical scales, such as smart water distribution systems. However, the design of this control approach requires accurate system model, which is normally difficult to obtain. In this work, we assume a digital twin of the original system can be obtained by some modeling and simulation tools, for example, Simscape in MATLAB/Simulink for water distribution systems, and thus present an event‐triggered controller design approach. We first present the design of such an event‐triggered controller for the digital twin; and then analyze the condition under which if both the original system and its digital twin can be satisfied, the same controller can work for the original system and some type of stability can be guaranteed at the same time. The proposed approach is applied in a smart water distribution system to show the feasibility.

Community Detection Using Modularity Optimization Method For Catchment Classification

A general framework for catchment classification may be helpful for more accurate and efficient modeling of hydrologic systems, as well as to improve communication between hydrology researchers and those in other disciplines. There are plethora numbers of methods applied for catchment classification, but in these years, recent studies are implementing the complex networks concept for classification purposes. The community structure methods which are complex networks-based methods are focus mainly to classify catchments. Hence, the efficiency of complex network ideas, especially using the methods of community structure is examined in this study. Specifically, the modularity optimization method that is one of the community structure methods is applied to classify 218 stream-gauges stations in entire Australia that covers a large variety of hydroclimatic, topographic, geomorphic, soil usage, and climatic parameters. In the present study, the applicability and the efficiency of the community structure concept is validated by the proposed method. The classification of Australian catchments was further assessed with threshold value of 0.8, which resulted formation of nine communities with at least 9 stations in a community which combine to have almost 77% of the total number of stations (165 out of 218). All nine selected communities were also examined in terms of the flow characteristics (i.e. flow mean and flow covariance) and the catchment characteristics (i.e. drainage area, elevation and stream length). The catchment behaviors for each selected communities were also interpreted in terms of distance and correlation relationship, which give some useful insights towards generalization of hydrologic framework.

Analysis of a domestic air heat pump integrated with an air-geothermal heat exchanger in real operating conditions: The case study of a single-family building

The work presents an in-depth analysis of the real performance of an air heat pump unit installed in a single-family building and quantitatively determines the reduction in energy consumption achieved by the integration of an air-geothermal heat exchanger. The case study is a single-family building located in northern Italy. It is equipped with a heating, ventilation, and air conditioning (HVAC) system consisting of an air-geothermal heat exchanger coupled with a compact air source heat pump supported by an electric heater installed in a thermal storage and devoted to cover the peak demand. The main objectives of the work are to analyse the HVAC performance under real operating conditions compared to the design values and to highlight the existence of factors that affect this performance. The analysis was carried out by developing an accurate model of the heat pump system based on the real operating data acquired by the on-board monitoring system. The results confirmed the influence of the air-inlet conditions (temperature and humidity) and of the control strategy (more specifically, the ventilation flow rate) on the unit performance in both the heating and cooling modes. In particular, the analysis showed that minimising indoor ventilation in the case of low outdoor temperatures is of paramount relevance not only because it reduces the indoor heat demand, but also because it maintains the unit efficiency close to the design values. Furthermore, the installation of a geothermal heat exchanger guarantees a reduction in electricity consumption of approximately 30%.