Exact Model Research Articles

A Review of Research on Diagnosability of Control Systems Based on Structural Analysis

Structural analysis, a model-based fault diagnosis approach, has been extensively highlighted since it does not depend on an exact analytical system model. The structural analysis approach focuses on obtaining the required information for fault detection and fault-tolerant control from the structural model of the control system. Diagnosability is a design attribute that indicates the fault diagnosis capabilities. Diagnosability design not only guarantees that the structure of the control system gives adequate fault information but also that the control system achieves the fault diagnosis with the least cost and the most excellent efficiency. In this paper, in combination with the research progress of structural analysis, the study first designs the structural diagnosability model of the control system based on structural analysis; secondly, the essential techniques of diagnosability evaluation and diagnosability design based on structural analysis are analyzed and summarized, and the diagnosability index system based on structural analysis is designed based on the structural detectability and structural isolation; this lays the foundation for the development of the diagnosability; lastly, the study summarizes the problems of diagnosability research based on structural analysis and the future development direction.

An Improved Gray Wolf Algorithm for Radiation Near-Field Source Localization in Exact Model

An Improved Gray Wolf Algorithm for Radiation Near-Field Source Localization in Exact Model

Charged anisotropic compact stellar models under gravitational decoupling scheme

The aim of this paper is to obtain new physically admissible exact anisotropic stellar models via gravitational decoupling technique. This will be done by taking Korkina and Orlyanskii model solution as a seed solution and extend it to an anisotropic domain via gravitational decoupling through the minimal geometric deformation (MGD) approach. We analyze the physical properties of four compact star models Her [Formula: see text] (Mass [Formula: see text], [Formula: see text][Formula: see text]km), LMC [Formula: see text] ([Formula: see text], [Formula: see text][Formula: see text]km), PSR [Formula: see text] ([Formula: see text], [Formula: see text][Formula: see text]km) and PSR [Formula: see text] ([Formula: see text], [Formula: see text][Formula: see text]km). We demonstrate the stability conditions of the obtained solution by plotting energy conditions, velocity of sound, adiabatic index and all the thermodynamic parameters and equilibrium conditions via TOV equation, etc. We found that the results are physically well behaved and also fulfill the requirement of stability criteria inside the anisotropic fluid sphere. We study the physical features of the solutions in detail, analytically and graphically for the four compact stars Her [Formula: see text], LMC [Formula: see text], PSR [Formula: see text] and PSR [Formula: see text]. Based on the results we realized that the extended gravitational decoupling technique is very effective for the analysis of the interior of the compact stellar structures.

A Nonlinear Critical Surface With Input Constraints on Costate-Adaptive Dynamic Programming

In this brief, a costate-adaptive dynamic programming algorithm is presented to solve a class of nonlinear critical surface problems with input constraints. Different from the traditional-dual heuristic dynamic programming, costate-ADP does not rely on the exact system model, and can incubate the model-neural network through data catalysis, avoiding the super-dimensional calculation in the iterative process. Furthermore, the critical nonlinear surface with input constraints is transformed into the optimal action sequence for solving the non-quadratic HJB equation with discount factor. The costate function replaces the integral term in the repeated iterations. The algorithm is proved to be effective in determining the security properties of a class of structures.

Features of backward waves and negative refraction in absorbing bi-isotropic media and metamaterials

ABSTRACT Conditions of the excitation of backward electromagnetic waves and negative refraction in absorbing bi-isotropic layered media and metamaterials are investigated. Based on the exact analytical model, the features determined by absorption and inhomogeneity of the waves and possible metamaterial properties of the medium are ascertained. It is shown that only simultaneous realization of backward, backward with respect to the boundary between two media, and negative refraction is possible for each of the proper waves in a bi-isotropic layer. The conditions are obtained when two (one transmitted and one reflected waves) or all the four proper waves in the layer are backward and characterized by negative refraction on boundaries. Taking into account the calculation of Pointing’s vector and energy dissipation of the waves, it is shown that the well-known limitations on the chirality and nonreciprocity parameters require a clarification for the considered media.

A theoretical investigation on heat transport feature of Sutterby nanofluid flow above a conical surface

ABSTRACT Theoretical analysis is performed to examine the heat convection in hydromagnetic Sutterby nanofluid flow across a cone in the attendance of Darcy-Forchheimer and modified temperature flux effects. The enhanced heat transmission was investigated using Maxwell and interfacial layer (nanolayer) approaches by considering ethylene glycol (EG) based spherical copper (Cu) nanoparticles. An exact model is developed and verified numerically using the bvp5c MATLAB scheme. Simultaneous outcomes are obtained for Maxwell and nanolayer approaches. Pertinent parameters influence on momentum and energy fields is examined via plots and tables. The nanolayer approach reported higher thermal conductivity than the Maxwell approach. Also, modified heat flux commendably improves the heat diffusion rate of Sutterby nanofluid without affecting the wall resistance.

Using the Exact Equivalent π-Circuit Model for Representing Three-Phase Transmission Lines Directly in the Time Domain

This paper presents a novel three-phase transmission line model for electromagnetic transient simulations that are executed directly within the time domain. This model relies on distributed and frequency-dependent parameters, as well as employs modal transformation for its implementation. The single-phase model of the exact equivalent π-circuit is utilized for each propagation mode. This model combines discrete components, such as resistors, inductors, and capacitors, to accurately emulate the transmission line behavior via linear circuit elements. This model can be seamlessly integrated into various electrical circuit simulation software, thus allowing easy utilization and incorporating time-varying elements to analyze transmission lines. The JMarti model, which comes by default in the Alternative Transient Program, and the numerical Laplace transform method implemented in MATLAB were utilized to validate the proposed solution across various scenarios. An advantage of this model is its independence from the prior calculation of travel time and its exemption from convolutions, inverse Laplace transforms, and Fourier transforms, thus streamlining the simulation process.

Application of machine learning methods for the prediction of roll force and torque during plate rolling of micro-alloyed steel

Machine learning technique is extensively used to establish the relationship between non-linear data sets which cannot be described mathematically and thus an exact analytic model is either intractable or too time-consuming to develop. During hot rolling, the effect of process parameters that cannot be captured in mathematical models, such as roll dimensions and its wear, the inter-pass time between rolling passes, temperature variation has been incorporated using multivariate supervised machine learning technique for accurate prediction of roll force and torque during plate rolling of micro-alloyed steel. An ensemble method was used to combine various machine learning techniques and average them to develop one final predictive model. K-cross validation of the model was carried out to validate the results and ensure the model gets the correct pattern of data. Root mean square error of ensemble roll force model was compared with roll force calculation using Sims theory. It was found that the machine learning model can predict the roll force and torque accurately as it takes care of various non-linear process variables which cannot be accounted for mathematically. The R-value of the machine learning model was >98 %, whereas it was 92.2 % for roll force calculation using Sims theory.

Empirically developed model of the stirring-controlled Belousov–Zhabotinsky reaction

The Belousov–Zhabotinsky chemical reaction is famous for its oscillatory behavior occurring due to inherent physicochemical processes. Oscillations of the Belousov–Zhabotinsky reaction may be periodic or chaotic, depending on parameters, and these dynamics can be controlled by external impacts such as stirring. The complex behavior, the variability of the oscillatory modes, and controllability make the Belousov–Zhabotinsky reaction an important research subject in many fields of science, including various applications such as chemical computing or smart materials. One of the key limitations for the practical application of this oscillatory reaction is the absence of its exact mathematical model which can reproduce dynamics close to the real chemical process. This work aims to fill this gap by designing a novel model of the Belousov–Zhabotinsky reaction based on experimental data. To solve this task, we designed a complex experimental setup based on a photometric station with controllable stirring mechanisms. We performed two experiments in batch reactors (cuvettes). In the first experiment, we investigated how constant stirring with different rates affects the reaction behavior, and in the second experiment, we periodically stirred the reaction and observed its response. As a result, we found that chemical oscillations in the Belousov–Zhabotinsky reaction are in both cases chaotic, their amplitude depends on stirring more than their period does, and they are highly non-stationary: in the course of the reaction, their sensitivity to stirring decreases, so the oscillations remain controllable only during the first hour. Using a novel two-stage differential equation reconstruction approach, we managed to develop a complex of local dynamical models for the stirring-controlled BZ reaction with good correspondence to the empirical data. The presented models are more consistent with experimentally observed Belousov–Zhabotinsky dynamics than earlier models. The developed model can be used in various applications and extended to a wide class of controlled oscillatory reactions.

Can Charged Stars be Singularity‐Free?

AbstractWhile the configuration of a static spherically symmetric distribution of perfect fluid in an electric field has received extensive attention in the literature, only a few works have investigated the conformally flat case. The model exhibits a remarkable property being regular or singularity free is proposed. This is unexpected given that Coulombic repulsion opposes the collapse of a star to the center generally making the center unreachable. The conformally flat geometry thus enables the approach to the center of a charged star as shown in our model. Presently there is no known reported exact model that satisfies all the elementary requirements for physical acceptability. Some reported models that have claimed to be physically reliable have been shown to be deficient. The conditions for the existence of physically relevant models and show that exact solutions satisfying the conditions exist is investigated. Then a model satisfying all the requirements with isotropic particle pressure graphically for a carefully chosen parameter space is exhibited. The model admits a barotropic equation of state. The interior spacetime is successfully matched to the exterior Reissner‐Nordstrom metric. The causality criterion as well as the adiabatic stability lower bound of Chandrasekar are shown to hold. The energy conditions are also valid within the stellar radius. Other interesting models are considered.

A coupled hygro-elastic 3D model for steady-state analysis of functionally graded plates and shells

Abstract This 3D coupled hygro-elastic model proposes the three-dimensional (3D) equilibrium equations associated with the 3D Fick diffusion equation for spherical shells. The primary unknowns of the problem are the displacements and the moisture content. This coupled 3D exact shell model allows to understand the effects of the moisture field in relation with the elastic field on stresses and deformations in different plates and shells. This model is specifically developed for configurations including functionally graded material (FGM) layers. Four different geometries are analyzed using an orthogonal mixed curvilinear reference system. The main advantage of this reference system for spherical shells is the degeneration of the equations to those for simpler geometries. The solving method is the exponential matrix method in the thickness direction. The closed-form solution is possible because of simply supported sides and harmonic forms for displacements and moisture content. The moisture content amplitudes are directly applied at the top and bottom outer faces through steady-state hypotheses. The final system is based on a set of coupled homogeneous second-order differential equations. The moisture field effects are evaluated for the static analysis in terms of displacement, strain, and stress components. After preliminary validations, used to better understand how to properly define the calculation of the curvature-related terms and FGM properties, four new benchmarks are proposed for several thickness ratios, geometrical data, FGM configurations, and moisture values imposed at the external surfaces. From the results, it is clear the accordance between the uncoupled hygro-elastic model and this new coupled hygro-elastic model when the 3D Fick diffusion law is employed. Both effects connected with the thickness layer and the embedded material are included in the 3D hygro-elastic analyses proposed. The 3D coupled hygro-elastic model is simpler than the uncoupled one because the 3D Fick diffusion law does not have to be separately solved.

Thick branes via higher order field theory models with exponential and power-law tails

In this work, we obtain exact thick brane models in 4+1 dimensions generated by higher order field theory kinks, inspired by specific potentials for phi ^{10} and phi ^{18} models. We verify that the geodesic equation along the fifth dimension confirms the confining effects of the scalar field on the brane for all of these models. These models provide new solutions with exponential and power-law tails which live in different topological sectors. We show that the resulting branes of specific exponential law models do not possess Z_2-symmetry. Furthermore, we examine the stability of the thick branes, by determining the sign of the w^2 term in the expansion of the potential for the resulting Schrödinger-like equation. It turns out that two of the three models of the phi ^{10} brane are stable, while another contains unstable modes for certain ranges of the model parameters. We also show that the brane solution from the specific phi ^{18} models are stable, while the others involve neutral equilibrium. The asymptotic behaviour of the brane solutions are also discussed.

Nonlinear auto regressive Elman neural network combined with unscented Kalman filter for data-driven dynamic data reconciliation in dynamic systems

Measurement data plays an important role in the control system, but the data collected by sensors often has measurement noise, which makes the states of the system cannot be accurately revealed. Unscented Kalman filter (UKF) is a highly accurate and robust filtering algorithm, but its limitation is the requirement of prior knowledge of the exact dynamic mathematical model, which is a critical issue to be addressed in practice. In this work, a data-driven dynamic data reconciliation scheme called nonlinear auto regressive Elman neural network (ENN) with exogenous inputs combined with UKF (NARX-ENN-UKF) is proposed, where nonlinear auto regressive ENN with exogenous inputs is used for dynamic data-driven modeling, and then UKF is applied for dynamic data reconciliation of the measurements based on the trained model. The scheme is applied to a DC/AC inverter experimental system and a self-developed sliding electrical contact experimental system to verify the effectiveness of NARX-ENN-UKF.

Identification of SH2 Domain-Containing Protein 3C as a Novel, Putative Interactor of Dipeptidyl Peptidase 3.

Dipeptidyl peptidase 3 (DPP3) is a zinc-dependent exopeptidase with broad specificity for four to eight amino acid residue substrates. It has a role in the regulation of oxidative stress response NRF2-KEAP1 pathway through the interaction with KEAP1. We have conducted stable isotope labeling by amino acids in a cell culture coupled to mass spectrometry (SILAC-MS) interactome analysis of TRex HEK293T cells using DPP3 as bait and identified SH2 Domain-Containing Protein 3C (SH2D3C) as prey. SH2D3C is one of three members of a family of proteins that contain both the SH2 domain and a domain similar to guanine nucleotide exchange factor domains of Ras family GTPases (Ras GEF-like domain), named novel SH2-containing proteins (NSP). NSPs, including SH2D3C (NSP3), are adaptor proteins involved in the regulation of adhesion, migration, tissue organization, and immune response. We have shown that SH2D3C binds to DPP3 through its C-terminal Ras GEF-like domain, detected the colocalization of the proteins in living cells, and confirmed direct interaction in the cytosol and membrane ruffles. Computational analysis also confirmed the binding of the C-terminal domain of SH2D3C to DPP3, but the exact model could not be discerned. This is the first indication that DPP3 and SH2D3C are interacting partners, and further studies to elucidate the physiological significance of this interaction are on the way.

Numerical Simulation Study on Frost Heave during the Freezing Phase of Shallow-Buried and Undercut Tunnel Using the Freeze-Sealing Pipe Roof Method

This article begins with the engineering geological conditions and freezing design scheme of the Gongbei Tunnel’s underground excavation section, then applies the mathematical model theory of the horizontal freezing-tunnel-formation freezing temperature field and frost heave displacement field, and builds a coupled two-dimensional finite element calculation model. The development law of the frozen soil curtain and the variation law of frost heave displacement during the freezing phase were studied by comparing on-site observation data. According to the findings of this study, the construction of the artificial frozen curtain is mostly based on two types of freezing tubes that freeze the soil between jacked pipes and seal the water. At 90 days, the thickness of the frozen soil curtain ranges from 2.32 m to 2.58 m, guaranteeing that its strength fulfills water-sealing safety criteria. The distribution and variation of frost heave displacement are highly related to engineering geological circumstances, the freezing scheme, the frozen soil curtain development process, and the pipe curtain structure. The maximum vertical frost heave displacement value at any time is located at the centerline, which is 155.67 mm at 90 d. The numerical simulation findings are acceptable and can potentially be utilized for predicting frost heave in subsequent projects. More research is required to effectively represent complicated working conditions and to develop more exact large-scale numerical models for tunnel excavation, support structure building, and other situations.

On the robustness of force estimation methods for robot manipulators: An experimental study

Force control of robot manipulators has been extensively studied for the last decades. Whether a direct or an indirect approach is pursued, it is always desirable to know the contact forces/torques, which are usually attained through force sensors. However, there are a variety of reasons for which it may not be desirable to employ them. For instance, they are expensive and not free of noise, and for some applications, they might just be too big to be a practical solution. Following these reasons, there have been many efforts to develop estimation techniques. Some of the main challenges are the need of exact robot models as well as position, velocity, and even acceleration measurements. In this work, a compilation of some of the most employed approaches developed in the last two decades is presented, along with the design of a novel force estimation algorithm. The proposed method is a modification of the well-known Generalized Momentum approach, with the advantages of finite-time convergence and improved performance. A comparative analysis is carried out to elucidate which of the methods delivers a better performance based on two key assumptions presented at the experimental implementation: 1) the robot model is accurate, and 2) the estimated forces/torques should be employed in a control scheme without a previous closed-loop stability analysis. Both assumptions are meant to test the robustness of the different approaches since the former is not realistic, while the latter is not advisable because no separation principle is valid for nonlinear systems. However, from a practical point of view, it is interesting to substitute force measurements with no further analysis. The experimental outcomes show closed-loop stability in all cases.

Strain Gauge Neural Network-Based Estimation as an Alternative for Force and Torque Sensor Measurements in Robot Manipulators

When a robotic manipulator interacts with its environment, the end-effector forces need to be measured to assess if a task has been completed successfully and for safety reasons. Traditionally, these forces are either measured directly by a 6-dimensional (6D) force–torque sensor (mounted on a robot’s wrist) or by estimation methods based on observers, which require knowledge of the robot’s exact model. Contrary to this, the proposed approach is based on using an array of low-cost 1-dimensional (1D) strain gauge sensors mounted beneath the robot’s base in conjunction with time series neural networks, to estimate both the end-effector 3-dimensional (3D) interaction forces as well as robot joint torques. The method does not require knowledge of robot dynamics. For comparison reasons, the same approach was used but with 6D force sensor measurements mounted beneath the robot’s base. The trained networks showed reasonably good performance, using the long-short term memory (LSTM) architecture, with a root mean squared error (RMSE) of 1.945 N (vs. 2.004 N; 6D force–torque sensor-based) for end-effector force estimation and 3.006 Nm (vs. 3.043 Nm; 6D force–torque sensor-based) for robot joint torque estimation. The obtained results for an array of 1D strain gauges were comparable with those obtained with a robot’s built-in sensor, demonstrating the validity of the proposed approach.

A Chattering-Suppression Sliding Mode Controller for an Underwater Manipulator Using Time Delay Estimation

Underwater manipulators are pivotal in diverse applications, encompassing underwater pipeline repairs, salvage operations, and scientific sampling missions. However, attaining high-precision control mandates the establishment of precise mathematical models. Specifically, developing an accurate dynamic model for the underwater manipulator affixed to underwater robots proves exceptionally demanding, primarily attributed to the uncertainties arising from water currents and the perturbations induced by the vehicle’s motion. This paper presents a novel sliding mode controller with time delay estimation for the high-precision control of an underwater manipulator mounted on an underwater vehicle. The presented controller circumvents the requirement for an exact mathematical model of the underwater manipulator, enhancing its control precision even without detailed modeling information. Moreover, the proposed controller guarantees robustness against uncertain water flow disturbances, effectively preserving the performance of the underwater manipulator under challenging underwater conditions. The proposed controller comprises two key elements. Firstly, a time delay estimator is employed to estimate the system states using feedback from intentional time-delayed control inputs and a pre-designed matrix. Additionally, a specially designed reaching law is incorporated to reduce reaching time and prevent chattering in the sliding mode control. The controller offers several advantages, including easy implementation, insensitivity to model uncertainties, and robustness in the underwater environment. Simulations and experiments demonstrate the ability of the proposed controller to effectively mitigate disturbances, eliminate chattering, and ensure precise trajectory-tracking performance. The experiments are conducted on a fully self-developed 2-degree-of-freedom (2-DOF) underwater manipulator, showcasing the superiority of the proposed controller over other alternatives.

The Impact of Uncertainty on the Control of a Multi-Axis Wave Energy Converter

As global energy demands and climate concerns continue to grow, the need for renewable energy is becoming increasingly clear and wave energy conversion (WEC) systems are receiving growing interest. Over the years, many WEC systems have emerged but most of these systems are designed to extract energy from a single direction of motion. In reality, there are six degrees of freedom that a conversion device can potentially harness and a device that can operate on multiple axes, should be able to more effectively and consistently produce power. However, the design and control of the power take off (PTO) system for a multi-axis device is challenging due to the system complexity and nonlinearity. WEC systems often utilize optimal control techniques for PTO operation and leverage a prediction of the upcoming wave force to ensure power optimization. Prior work has clearly demonstrated that high power production can be achieved when an exact system model is used and the upcoming wave conditions are known, but uncertainty in the underlying model or the wave prediction can degrade performance. PTO control on a multi-axis WEC must leverage predictions of forces in multiple directions and if model predictive strategies are used, must leverage a simplified model of the WEC dynamics to be able to optimize in real time. The uncertainty in these predictions and the model could severely degrade the WEC’s power output. This work examines the control of a multi-axis WEC system, TALOS, and leverages machine learning to predict wave forces over the upcoming time horizon. TALOS is a point-absorber type WEC with multi-axis PTO system. The PTO uses a heavy ball that is attached to the hull with springs and hydraulic cylinders. When the hull is pushed by the external waves, the relative motion between the ball and hull moves the hydraulic cylinders causing them to pump a fluid through a circuit, thereby driving a hydraulic motor to produce electricity. This design has shown promising results in energy output but is more challenging to control since the PTO can move over six degrees of freedom. This paper seeks to quantify wave prediction uncertainty and its seasonal variation and to examine the impact of the uncertainty of the prediction on a model predictive controller’s ability to optimize the power output of TALOS.

Intelligent Modelling of the Real Dynamic Viscosity of Rubber Blends Using Parallel Computing.

Modelling the flow properties of rubber blends makes it possible to predict their rheological behaviour during the processing and production of rubber-based products. As the nonlinear nature of such complex processes complicates the creation of exact analytical models, it is appropriate to use artificial intelligence tools in this modelling. The present study was implemented to develop a highly efficient artificial neural network model, optimised using a novel training algorithm with fast parallel computing to predict the results of rheological tests of rubber blends performed under different conditions. A series of 120 real dynamic viscosity-time curves, acquired by a rubber process analyser for styrene-butadiene rubber blends with varying carbon black contents vulcanised at different temperatures, were analysed using a Generalised Regression Neural Network. The model was optimised by limiting the fitting error of the training dataset to a pre-specified value of less than 1%. All repeated calculations were made via parallel computing with multiple computer cores, which significantly reduces the total computation time. An excellent agreement between the predicted and measured generalisation data was found, with an error of less than 4.7%, confirming the high generalisation performance of the newly developed model.