Parameter Identification Research Articles

An Identification-Based Control Method for an Overhead Crane with a New Combined Sensor Placement

This paper is devoted to an automatic control method for an overhead crane under the current parametric uncertainty of the crane, transported cargo, and exogenous disturbances. The control objective is to move the cargo in the horizontal plane to a point ensuring the final delivery of the cargo to the designated place while parrying the angular oscillations of the suspension and reaching given dynamic characteristics. The approach is based on a control scheme with a current parametric identification algorithm, an implicit reference model, and “simplified” adaptability conditions to track cargo movements directly. The control law generates a given trolley speed for the servo drive. The passport data of the crane installation are used to select the control law parameters. Unlike previous publications on the topic, the solution proposed below is simpler, more reliable in terms of operation, and less expensive. This is achieved by placing a combined sensor (an accelerometer with an angular rate sensor (ARS)) on a suspension cable near the crane trolley and applying, first, an algorithmic solution without the preliminary calculation of the ARS drift and, second, a current parametric identification procedure of higher efficiency. Computer simulation results are provided to confirm these advantages of the new solution. A similar example is implemented on an experimental installation.

Static Model of the Underwater Soft Bending Actuator Based on the Elliptic Integral Function

Underwater soft manipulators are increasingly used for grasping underwater organisms and cultural relics due to their compliance and ability to protect delicate objects. Unlike rigid manipulators, these soft manipulators feature underwater soft bending actuators that deform under external forces, making their shape and output force challenging to predict. Consequently, classical beam theory is no longer applicable. To address these issues, this article models the underwater soft bending actuator as a cantilever beam and establishes its shape and output force using elliptic integral functions. Additionally, this article proposes a method for determining the unknown parameters of the driver shape and output force model using optimization techniques and introduces a solution process based on the particle swarm optimization framework. Given the role of tensile and bending stiffness in the actuator’s performance, this paper employs a parameter identification method based on length and bending angle information. Tests demonstrate that the proposed method effectively estimates the deformation and output force of the underwater soft bending actuators, confirming its accuracy.

Analysis of Factors Affecting the Seismic Performance of Widened Flange Connections in Mid-Flange H-Beams and Box Columns

Following the Northridge and Kobe earthquakes, research has increasingly focused on achieving high ductility in beam-to-column connections. This study investigates the seismic performance of connections featuring widened beam-end flanges in mid-flange H-beams and box columns, an area with limited prior research compared to I-section columns and narrow-flange H-beams. Detailed finite element modeling using ABAQUS 6.1.4 demonstrates that widened beam-end flanges significantly improve bending capacity and ductility by relocating the plastic hinge away from the connection, thereby enhancing seismic resilience. Key findings include the identification of optimal design parameters: flange length ranging from 0.55 to 0.75 times the beam flange width, beam flange cutting length between 0.36 and 0.39 times the beam depth, and flange cutting depth from 0.19 to 0.23 times the beam flange width. These parameters ensure effective plastic hinge development and improved structural performance. This study introduces a novel approach that emphasizes geometric optimization over material-based enhancements, offering a cost-effective and practical solution for improving seismic performance and extending previous research insights.

Deep Learning with Crested Porcupine Optimizer for Detection and Classification of Paddy Leaf Diseases for Sustainable Agriculture

India has a vast number of inhabitants and the main food source distribution is from agriculture. Agricultural lands are being demolished generally owing to plant and crop illnesses. The detection of plant diseases by using image processing models can aid agriculturalists in defending the farming area from damaging or affecting it. Paddy is the main harvest worldwide. Early recognition of the paddy diseases at dissimilar phases of development is very vital in paddy production. However, the present manual technique in identifying and classifying paddy diseases needs a very educated farmer and is time-consuming. Deep learning (DL) is an effectual research area in the classification of agriculture patterns where it can efficiently solve the problems of diseases identification. Therefore, the articles focus on the design and expansion of Deep Learning based Crested Porcupine Optimizer for the Detection and Classification of Paddy Leaf Diseases (DLCPO-DCPLD) method for Sustainable Agriculture. The main aim of the DLCPO-DCPLD method use DL method for the recognition and identification of rice plant leaf diseases. To accomplish this, the DLCPO-DCPLD technique performs the image pre-processing using Median Filtering (MF) to recover the excellence of the input frames. Next, the ConvNeXt-L method is applied for extraction of feature vectors from the pre-processed images. Also, the Conditional Variational Autoencoder (CVAE) model is utilized for the automated classification of Paddy Leaf diseases. Eventually, the hyperparameter tuning of the CVAE technique is accomplished by implementing the Crested Porcupine Optimizer (CPO) technique. To safeguard the enhanced predictive results of the DLCPO-DCPLD method, a sequence of experimentations is implemented on the benchmark dataset. The experimental validation of the DLCPO-DCPLD method portrayed a superior accuracy value of 99.12% over existing approaches.

Parameter estimation and singularity of laws on the path space for SDEs driven by Rosenblatt processes

In this paper, we study parameter identification for solutions to (possibly non-linear) SDEs driven by additive Rosenblatt process and singularity of the induced laws on the path space. We propose a joint estimator for the drift parameter, diffusion intensity, and Hurst index that can be computed from discrete-time observations with a bounded time horizon and we prove its strong consistency under in-fill asymptotics with a fixed time horizon. As a consequence of this strong consistency, singularity of measures generated by the solutions with different drifts is shown. This results in the invalidity of a Girsanov-type theorem for Rosenblatt processes.

Modeling and Verification of Cable-Hole Transmission Tension Ratio Considering the Cable Lateral Extrusion

Cable-hole transmission is widely applied in cable-driven mechanisms to reduce the mechanical size. However, the driving tension is attenuated with the cable threading through the hole caused by uncertain factors such as local deformation, friction, and other effects, and errors in cable-hole transmission occur. To improve the transmission accuracy of cable-driven mechanisms, a tension distribution model considering the cable lateral extrusion is established. Then, an analytical tension ratio of the cable-hole transmission is derived based on the perturbation method and tension distribution model. Parameters of the tension ratio are identified using a particle swarm optimization algorithm. An adaptive tension control method considering the cable lateral extrusion is designed and compared with the method excluding the cable lateral extrusion in the cable-hole transmission. Finally, a cable-hole transmission experimental device was constructed to verify the tension ratio, parameter identification, and servo control method of the cable-hole transmission. The results show the motion control accuracy of the cable-driven mechanism can be significantly improved with the tension ratio considering the cable lateral extrusion. Compared to the case excluding the cable lateral extrusion, the errors in cable-hole transmission considering the lateral extrusion are reduced by an order of magnitude, and the tension vibration is significantly weakened.

Shear Band Formation with Split Hopkinson Bar Experiments

The essence of dynamic failure is closely linked to dramatic shear deformations which often lead to the formation of adiabatic shear bands (ASB). Under high loading velocities and the subsequent rapid temperature increase, the localization of shear strain is crucial in view of safety issues of systems in mechanical and aircraft engineering, especially with respect to fast rotating components and diverse crash scenarios. In this research, we perform high speed impact tests at the split Hopkinson pressure bar (SHPB) setup and use particular hat-shaped specimen geometries that resemble the stresses and failure conditions at the component level.In the first step, we specify a notched specimen geometry using finite element (FE) simulations to ensure pure shear. Further, quasi-static compressive tests and a series of impact tests at high strain rates of 103−104s−1 are conducted on specimens manufactured from a fine-grain structural steel with the properties of S355. Optical microscopy and electron backscatter diffraction (EBSD) of the sheared zones unveil significant localization to maximal shear strains of about 0.9 accompanied by grain refinement by factors 5 to 14. The displacements across the surface of the specimens are captured with subset-based local digital image correlation (DIC) during the impact time, and serve as an objective to validate a viscoplastic constitutive relationship. More precisely, the deformation distribution is accurately reproduced by the widely recognized Johnson-Cook (JC) model, which features an enhanced description of damage evolution. Thus, combining experimental and characterization techniques, continuum mechanics and reasonable optimization strategies for the identification of model parameters provides an efficient approach for comprehensive insights into the strain localization behaviour and its impact on the mechanical performance of S355 under extreme strain rates and deformations.

Autoregressive distributed lag-based dynamic uniformity modeling and monitoring approaches for superconductor manufacturing

ABSTRACT High-temperature superconductors (HTS), known for their high efficiency and low energy loss, have found profound applications across various fields, driving the demand for long, uniformly performing tapes. However, ensuring uniform performance over extended lengths of HTS tapes, often characterized by the consistency of critical current, remains challenging due to fluctuations in growth conditions during manufacturing. To elucidate the mechanisms underlying variations in tape uniformity and enable real-time monitoring of associated parameters, we propose an Autoregressive Distributed Lag (ADL)-based Dynamic Uniformity Modeling and Monitoring (ADUM2) approach. This method integrates uniformity measurement, the identification of critical process parameters and real-time monitoring within the manufacturing process. The ADUM2 approach is applied to the advanced metal organic chemical vapor deposition (A-MOCVD) process, a pilot-scale method for superconductor manufacturing. Our model demonstrates superior performance compared to benchmark methods, accounting for over 80% of the total variance in the data and identifying 13 key process parameters influencing the uniformity of HTS tapes. This study offers significant insights into the high-temperature superconductor manufacturing process and holds the potential to facilitate the production of cost-effective, uniformly performing long superconducting tapes in the future.

A Review of Parameter Identification and State of Power Estimation Methods for Lithium-Ion Batteries

A Review of Parameter Identification and State of Power Estimation Methods for Lithium-Ion Batteries

Theoretical and experimental analysis of a nonlinear vibro-acoustic energy sink

This study is concerned with the experimental and theoretical analysis of a vibroacoustic nonlinear system devoted to absorption application. Based on the nonlinear energy sink principle, the dynamics of the system is reduced to two coupled degrees of freedom, one of which exhibits nonlinear behavior due to a large displacement mechanism. The set-up allows for precisely characterizing the nonlinear function of the mechanical system, first uncoupled and then coupled with an acoustic and linear resonator. Continuation procedures are performed experimentally and numerically, respectively using a phase-locked loop (PLL) and the asymptotic numerical method implemented in the MANLAB software package. The comparison between theoretical predictions and experimental results obtained on the uncoupled system allows for a parameter identification of the nonlinear resonator. Measurements on the coupled system highlight the energy pumping phenomenon, which can be predicted from numerical predictions in which identified parameters are introduced. The procedure aims at proposing analytical models for achieving efficient level attenuation, and providing design guidelines for sound reduction at high levels in confined spaces.

An experimental insight into a promising novel organic nonlinear optical single crystal Guanidinium 2,4-dichlorobenzoate (G24DCB)

Guanidinium 2,4-dichlorobenzoate (G24DCB), a new chloro substituted benzoic acid based organic single crystal is synthesized, grown and added to the guanidinium family of single crystals. Such G24DCB crystal was obtained by a versatile slow evaporation solution growth technique. The analysis of single crystal X-ray diffraction examined the phase identification and determination of lattice parameters for the synthesized compound. The G24DCB crystal has monoclinic system and adopts space group P21/c. Powder X-ray diffraction explores the studied crystal's crystalline characteristics. The study of Fourier Transform-Infrared (FTIR) spectroscopy examined the necessary vibrational assignments for the formation of the structure of G24DCB. The UV–Visible-NIR analysis entails the determination of the cut-off wavelength of the G24DCB compound from its transmittance spectrum and the computation of other related optical parameter values. Photoluminescence analysis was employed to investigate the studied crystal's emission characteristics. The thermal stability of the G24DCB crystal was examined through thermogravimetric analysis and differential thermal analysis. The laser-induced damage threshold of the crystal was determined using a Nd:YAG laser. The various mechanical attributes exhibited by the synthesized compound were elucidated by microhardness studies and it corresponds to a soft material type. The various third order nonlinear optical characteristics were probed by Z scan study. The positive results of the aforementioned studies suggest the emerging significance possessed by the novel title crystal Guanidinium 2,4-dichlorobenzoate in nonlinear optical applications.

Критериальное поле и диагностические инструменты определения эмоционального благополучия детей дошкольного возраста в семье и образовательной организации

<p>The article is devoted to the important problem of identification of the emotional well-being of preschool children in the family and educational organization. Basing on the analysis of existing approaches to this problem in Russian psychological and pedagogical science, the authors have identified the criteria field of the concept of "emotional well-being", which is a complex level structure of interrelated criteria and indicators: emotional background (manifestation of emotions), the ability to control emotions (emotional stability) and emotional perception of situations (emotional attitude to situations and events). Such manifestations in the behavior and activities of a child, depending on the conditions of his life in a family or educational organization, are negative or positive. The complexity of the concept of "emotional well-being" and the criteria field of its identification in childhood requires an integrated approach to the development of diagnostic tools. The authors announced a developed set of diagnostic tools, which includes methods that allow to record external indicators of the emotional state of a child, to identify the child's internal emotional attitude to current or predicted events, as well as to obtain an external indirect assessment of the child's subjective state and the child's subjective attitude to situations in the family and educational organization. It is noted that the announced methods can be used in the practice of preschool education in order to support psychological and pedagogical development of preschool children including various target groups, as well as in the targeted psychological assistance of families with preschool children.</p>

Silicone Rubber-Packaged FBG Sensing Information and SSI-COV-Recognized Modal Parameters Motivated Damage Identification in Pipe Structures

Pipes are the main structures serving as the lifeline for oil and gas transportation. However, they are prone to cracks, holes and other damages due to harsh working environments, which can lead to leakage incidents and result in significant economic losses. Therefore, the development of structural health monitoring systems with advanced online diagnostic methods is of great importance for identifying local damages and assessing the safety state of pipe structures. These efforts can guide rapid repairs and ensure the continuous, efficient and cost-effective transportation of oil and gas resources. To address this problem, this paper proposes the development of a pipe monitoring system based on quasi-distributed fiber Bragg grating (FBG) sensing technology. The SSI-COV method is employed to process the sensor responses and extract the modal parameters of the structure. Based on this foundation, an enhanced damage identification index is proposed, which mitigates the effects of support and excitation positions on damage identification. The pipe structure can be regarded as a continuous super-statical beam, and based on its structural symmetry, a unit structure, specifically a stainless-steel pipe with fixed ends, is regarded as the experimental subject. Impact experiments have been conducted to analyze its behavior in both undamaged and damaged states. The research indicates that by using the proposed modal parameter identification method and the ASMDI damage index, ASMDI exhibits peak values at damage locations of the pipe structure. This allows for the identification of structural damage with high accuracy, fast processing efficiency and strong robustness. The study provides an effective and reliable damage diagnosis method, which can contribute to the refinement and visualization of pipe structural health monitoring systems.

Low-complexity recursive constrained maximum Versoria criterion adaptive filtering algorithm

Linearly-constrained adaptive filtering algorithms have emerged as promising candidates for system estimation. The existing methods such as the constrained least mean square algorithm rely on mean square error based learning, which delivers suboptimal performance under non-Gaussian noise environments. Therefore, the recursive constrained maximum Versoria criterion (RCMVC) algorithm has been derived and is robust against impulsive distortions. Nonetheless, RCMVC suffers from a notable computational overhead stemming from matrix inversion operations. To circumvent this issue, utilizing the weighting method and the dichotomous coordinate descent (DCD) iteration method, this paper derives a low-complexity version of the RCMVC algorithm called DCD-RCMVC, which alleviates the requirement of matrix inversion and enhances the estimation accuracy and robustness against non-Gaussian interference. Furthermore, we also present a comprehensive theoretical analysis of the DCD-RCMVC algorithm, encompassing discussions on its equivalence, convergence properties, and computational complexity. Simulations performed for system identification problems indicate that the DCD-RCMVC algorithm outperforms the existing state-of-art approaches.

A Hybrid Sobolev Gradient Method for Learning NODEs

The inverse problem of supervised reconstruction of depth-variable (time-dependent) parameters in ordinary differential equations is considered, with the typical application of finding weights of a neural ordinary differential equation (NODE) for a residual network with time continuous layers. The differential equation is treated as an abstract and isolated entity, termed a standalone NODE (sNODE), to facilitate for a wide range of applications. The proposed parameter reconstruction is performed by minimizing a cost functional covering a variety of loss functions and penalty terms. Regularization via penalty terms is incorporated to enhance ethical and trustworthy AI formulations. A nonlinear conjugate gradient mini-batch optimization scheme (NCG) is derived for the training having the benefit of including a sensitivity problem. The model (differential equation)-based approach is thus combined with a data-driven learning procedure. Mathematical properties are stated for the differential equation and the cost functional. The adjoint problem needed is derived together with the sensitivity problem. The sensitivity problem itself can estimate changes in the output under perturbation of the trained parameters. To preserve smoothness during the iterations, the Sobolev gradient is calculated and incorporated. Numerical results are included to validate the procedure for a NODE and synthetic datasets and compared with standard gradient approaches. For stability, using the sensitivity problem, a strategy for adversarial attacks is constructed, and it is shown that the given method with Sobolev gradients is more robust than standard approaches for parameter identification.

Consensus Control of Leader–Follower Multi-Agent Systems with Unknown Parameters and Its Circuit Implementation

With the development and progress of Internet and data technology, the consensus control of multi-agent systems has been an important topic in nonlinear science. How to effectively achieve the consensus of leader–follower multi-agent systems at a low cost is a difficult problem. This paper analyzes the consensus control of complex financial systems. Firstly, the dynamic characteristics of the financial system are analyzed by the equilibrium points, bifurcation diagrams, and Lyapunov exponent spectra. The behavior of the financial system is discussed by different parameter values. Secondly, according to the Lyapunov stability theorem, the consensus of master–slave systems is proposed by linear feedback control, wherein the controllers are simple and low cost. And an adaptive control method for the consensus of master–slave systems is investigated based on financial systems with unknown parameters. In theory, the consensus of the leader–follower multi-agent systems is proved by the parameter identification laws and linear feedback control method. Finally, the effectiveness and reliability of the consensus of leader–follower multi-agent systems are verified through the experimental simulation results and circuit implementation.

Blade tip timing signals parameters identification based on Displacement Offset Decreased and Compressed Sensing

High-speed rotating blade vibration monitoring is of great significance for the safe operation of turbomachinery. Blade Tip Timing (BTT) is considered a promising technology in the field of blade vibration monitoring. The BTT signal analysis has the following problem, the displacement offset was caused by speed variation which affected the accuracy of the blade vibration parameter identification. Owing to the under-sampled characteristics of the BTT signals and multiple sensor data are required to identify the parameters. The accurate extraction of blade vibration characteristics is challenging. Thus, a vibration analysis method is proposed based on Displacement Offset Decreased and Compressed Sensing (DOD-CS). Firstly, the BTT signal pairwise is subtracted to reduce the impact of the displacement offset, and based on the subtractive results, a basis function is constructed to rebuild the dictionary matrix of the compressed sensing method. Then, we solved the signal sparse representation equation and obtained the spectrum estimation result, that is, the structure vibration parameters. The analysis of simulation data shows that the parameter identification accuracy of the DOD-CS method is higher than that of the OMP-CS (Orthogonal Matching Pursuit and Compressed Sensing) method, and the identification errors of frequency and amplitude are within 1% and 5%, respectively. The experimental results confirm the feasibility of the proposed method in identifying vibration parameters. Compared with existing methods, the innovation of the proposed method is that it eliminates the influence of displacement offset on the accuracy of parameter identification. For under-sampled signals, only two sensor data information are needed to complete spectrum estimation and parameter identification.

Theoretical modeling and parameter identification of balanced armature loudspeakers.

Theoretical modeling and parameter identification are essential for optimizing loudspeaker performance and enabling active control. Although relevant theories for moving-coil loudspeakers are well-developed, accurate theoretical modeling and parameter identification methods for balanced armature loudspeakers (BALs) are scant. This study proposes a model using the equivalent circuit method (ECM) for BALs, with consideration of the armature-suspension coupling as well as the non-piston vibration of the diaphragm. Based on the proposed ECM model, a time-domain identification algorithm utilizing measured voltage, current, and displacement data is established to identify the necessary parameters. Employing the theoretical model and proposed identification method, the model parameters of two different BALs are measured. Comparisons between experimental and numerical results demonstrate the accuracy and effectiveness of the proposed model and identification method in predicting impedance, displacement, and sound pressure responses.

Kinematic calibration of industrial robot using Bayesian modeling framework

Positioning accuracy of an end-effector is a crucial metric for evaluating industrial robot performance. Uncertainties in joint angles and joint backlash deviate actual angles from the designed nominal values to negate positioning accuracy. Most existing parameter identification methods overlook or not properly account for such uncertainties, leading to usually overconfident identification results. To this gap, the present study introduces a kinematic calibration methodology employing Bayesian parameter estimation to achieve identification of joint variables. New formulas based on data features of industrial robots for constructing the likelihood function are proposed, and model selection is applied to assess various likelihood functions for a tradeoff balance between complexity and accuracy. To evaluate the robustness of the proposed approach, identification of joint variables is conducted under different measurement noises. The position response of kinematic model is predicted based on the identified joint uncertainty information. The efficacy is verified through rigorous scrutiny involving both a numerical example and an engineering application. Results indicate that the proposed method exhibits satisfactory kinematic parameter identification accuracy and robustness. In addition, the uncertainty of parameters can be measured and the prediction of trajectory uncertainty intervals is realized simultaneously, which promotes the application of industrial robots in high-precision scenes.

A Parameter Identification and Control Method Based on Compensation Topology With Strong Misalignment Tolerance

A Parameter Identification and Control Method Based on Compensation Topology With Strong Misalignment Tolerance