Parameter Identification Method Research Articles

Hydrogen production efficiency enhancement for alkaline water electrolyzers operating at varying temperature via an adaptive multi-mode control

The low-load efficiency performance of alkaline water electrolyzers (AWEs) is poor so its operation range is limited. It is unfeasible for AWEs to follow the fluctuant renewable energy sources (RESs). Besides, the power fluctuation will lead to the electrolyzer temperature variation. The efficiency performance is negatively influenced. Focusing on these problems, an efficiency enhancement control strategy for AWEs operating at varying temperature is introduced in this paper. First, the efficiency model of AWEs is established. Then, a multi-mode adaptive control strategy is proposed to improve the hydrogen production efficiency of AWEs. Based on the U–I curves of AWEs, parameter identification method is used to obtain the key parameters of proposed efficiency model. Finally, the feasibility of the control strategy is experimentally validated on a 10 kW AWE. The experimental outcome shows that by applying the proposed method, the low-load hydrogen production efficiency of AWEs can be improved at different temperatures. At 15% rated load, the electrolyzer efficiency increases from 21.54% to 42.73% at 80°C. The operation range of the electrolyzer can be extended from 30% to 100%–21%∼100% of rated load at 80°C.© 2024 The Authors. Published by Elsevier Ltd.

Experimental Evaluation of Under Slab Mats (USMs) Made from End-of-Life Tires for Ballastless Tram Track Applications

The growing population of urban areas results in the need to deal with the noise pollution from the transportation system. This study presents experimental test results of static and dynamic elastic characteristics of under slab mats (USMs) according to the procedure of DIN 45673-7. Prototype USMs based on recycled elastomeric materials, i.e., SBR granules and fibres produced from waste car tires, are analysed. Vibration isolation mats with different thicknesses (10, 15, 20, 25, 30, and 40 mm), densities (500 and 600 kg/m3), and different degrees of space filling (no holes, medium holes, large holes) are considered. Moreover, a practical application of the laboratory test results of USMs in the design of ballastless track structures of two different types (with a concrete slab and longitudinal beams) is presented. Deflections of the rail and the floating slab system, as well as stresses acting on the mat, are determined according to EN 16432-2. The use of shredded rubber from recycled car tires as a material component of sustainable and environmentally friendly tram track structures may be one of the most effective ways to manage rubber waste within the current trend toward a circular economy, and this study intends to introduce methods for experimental identification and analytical selection of basic static and dynamic parameters of prototype USMs.

A multi-task learning based line parameter identification method for medium-voltage distribution network

Accurate line parameters are critical for and dispatch in distribution systems. External operating condition variations affect line parameters, reducing the accuracy of state estimation and power flow calculations. While many methods have been proposed and obtained results rather acceptable, there is room for improvement as they don’t fully consider line connections in known topologies. Furthermore, inaccuracies in measurement devices and data acquisition systems can introduce noise and outliers, impacting the reliability of parameter identification. To address these challenges, we propose a line parameter identification method based on Graph Attention Networks and Multi-gate Mixture-of-Experts. The topological structure of the power grid and the capabilities of modern data acquisition equipment are utilized to capture. We also introduce a multi-task learning framework to enable joint training of parameter identification across different branches, thereby enhancing computational efficiency and accuracy. Experiments show that the GAT-MMoE model outperforms traditional methods, with notable improvements in both accuracy and robustness.

Dynamic parameter identification method for wireless charging system of AUV based on multi-strategy nonlinear rime algorithm

Dynamic parameter identification method for wireless charging system of AUV based on multi-strategy nonlinear rime algorithm

Parameter Identification and Energy Dissipation Analysis of Premium Connections Based on the Iwan Model

The premium connection is an important section of the tubing column. Under intricate downhole conditions, axial vibration generates alternating loads that cause energy dissipation between the sealing surfaces of the premium connection, reducing sealing performance. To investigate this issue, the mutual conversion process of sticking, slipping, and macroscopic slipping stages between the sealing surfaces of the premium connection under axial loads must be assessed. In this study, a finite element analysis model of a taper–taper Φ88.9 mm × 6.45 mm P110 premium connection is developed based on the discrete Iwan model’s ontological relationship, and the sealing surface’s force–displacement hysteresis curve is obtained. The equivalent Iwan model for this particular premium connection is constructed by discretizing the hysteresis curve and identifying the model’s four sets of parameters. The correctness of the parameter identification method of the equivalent Iwan model is verified by comparing and analyzing the similarity of the two models. The energy dissipation in the sealing surfaces of the premium connection for different working conditions under dynamic loading is analyzed. This study reveals that the area similarity of the hysteresis curves of the two models is more than 92%, while the positional error is less than 2%. The sealing surface displacement amplitude of the premium connection is between 0.04 mm and 0.07 mm, while the sealing surface energy dissipation increases linearly, which may lead to a decline in sealing performance.

A method for dynamic parameter identification of an industrial robot based on frequency response function

AbstractHaving accurate values of the dynamic parameters is necessary to characterize the dynamic behaviors of mechanical systems and for the prediction of their responses. To accurately describe the dynamic characteristics of industrial robots (IRs), a new method for dynamic parameter identification is proposed in this study with the goal of developing a real IR dynamics model that combines the multibody system transfer matrix method (MSTMM) and the nondominated sorting genetic algorithm‐II (NSGA‐II). First, the multibody dynamics model of an IR is developed using the MSTMM, by which its frequency response function (FRF) is calculated numerically. Then, the experimental modal analysis is conducted to measure the IR's actual FRF. Finally, the objective function of the minimum errors between the calculated and measured eigenfrequencies and FRFs are constructed to identify the dynamic parameters of the IR by the NSGA‐II algorithm. The simulated and experimental results illustrate the effectiveness of the methodology presented in this paper, which provides an alternative to the identification of IR dynamic parameters.

A novel inertial parameter identification method for the ship-mounted Stewart platform based on a wave compensation platform

To meet the requirement of time-varying inertial parameter identification of control algorithms for shipborne Stewart wave compensation platforms, this study proposes an innovative inertial parameter identification method based on a non-inertial system. Unlike traditional Stewart identification methods with a fixed frame, the proposed method does not require designing excitation trajectories to consider the condition number of the observation matrix. Namely, the proposed method requires only performing two-dimensional dynamic parameter collection for parameter identification in a six-dimensional non-inertial motion. In addition, a coupling error dimension reduction method is developed to improve the identification accuracy under the condition of coupling motion. Finally, experiments are conducted to verify the effectiveness of the proposed identification method. The experimental results show that under the coupling motion conditions, the coupling errors of the corrected singularity points are reduced by 7.9–28.1% compared to before correction, and the axial force average error correction is below 5.32%.

Parameter identification of rock mass in the time domain

A time domain rock mass parameter identification method considering the Rayleigh damping is proposed in this paper. Starting with a set of initial parameter values, the algorithm solves the finite-element (FE) equations updating the parameters in each iterative step until the result converges to a local optimum. Numerical FE analysis, displacement sensitivity analysis, and the truncated singular value decomposition method are integrated into the solution algorithm for parameter identification. The technique introduced is verified in numerical studies with in-situ experiments of rock mass structures. Our results show that the approach, compared with conventional methods, can reduce ambiguities caused by inadequate data and provide more accurate insights into the subsurface for rock mass quality evaluation.

Optimization method of parameters inverse identification for hot deformation constitutive model of 2Cr13 martensitic stainless steel using genetic algorithm

Constitutive models are considered a key prerequisite to investigate the thermal deformation behavior of materials. Accurate identification of their parameters is crucial for enhancing the predictive precision of the models. A novel optimization method for accurate inverse identification of parameters using the genetic algorithm (GA) in the Modified Zerilli-Armstrong (MZA) model was proposed in this work. Under conditions of temperatures ranging from 1223 K to 1473 K at intervals of 50 K and strain rates of 0.01, 0.1, 1, and 5 s−1, uniaxial isothermal hot compression tests were performed on 2Cr13 martensitic stainless steel (MSS) employing a Gleeble-1500D thermal simulation test machine. Based on the experimental data, the conventional linear regression method was utilized to solve the MZA model of 2Cr13 MSS. Initial values for the material constants I1, S1, C5, and C6 to be optimized were assigned drawing from the calculated results. The GA-based iteratively optimized MZA (G-ZA) model was established by minimizing the mean squared error as an objective function between the experimental and predicted flow stress values. Compared to the MZA model, a significant enhancement in predictive performance was achieved with the G-ZA model, while good generalization was also demonstrated. Both models were successfully integrated into the Forge® finite element analysis software through the secondary development of user subroutines. Numerical simulation results indicated that the G-ZA model demonstrated a better agreement with the experimental data in predicting the load-displacement curve. This validated that a more accurate constitutive model for the hot deformation of 2Cr13 MSS can be effectively constructed using the GA-based parameter inverse identification strategy.

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.

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.

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.

Method for robot kinematic parameters identification based on position and orientation data obtained with laser tracker.

How to quickly and accurately identify the kinematic parameter errors is an important prerequisite for robot accuracy compensation. The laser tracker is used to measure and identify the kinematic parameters of the robot. The influence of laser tracker layout in robot pose measurement is analyzed, and the evaluation function of laser tracker layout is constructed to determine the optimal layout position. Then, the mapping relationship between the position and orientation deviation of the robot end and the deviation of each kinematic parameter is established, and the kinematic parameter identification model integrating the position and orientation information is constructed. The identification model is compared to the identification model based on position information to clarify the intrinsic reason of high accuracy. Next, aiming at the problem that the genetic algorithm is easy to prematurely converge to the local optimal solution, a hybrid genetic algorithm is constructed by introducing the simplex method to determine the optimal calibration pose set of the robot. On this basis, the results of robot kinematics parameter identification based on position measurement data, pose measurement data, and optimal calibration pose set are compared and analyzed through experiments, which verifies the effectiveness of the robot kinematics parameter identification model based on pose measurement data and optimization strategy.

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

Hysteresis dynamic modeling of 4-SPS parallel all-metallic isolator with spherical joints considering nonlinear micro-collision and interfacial friction

This work aims to identify ways to model a high-accuracy hysteresis dynamic model for an innovative 4-SPS parallel all-metallic isolator. Firstly, a three-dimensional contact model of spherical joints under different conditions (stretching and compression) is proposed, referred to as the integrated model of nonlinear micro-collision and interfacial friction (INCF model). Simultaneously, in conjunction with the nonlinear elastic recovery force, nonlinear damping force, and nonlinear hysteresis damping force, the high-accuracy hysteresis dynamic model of the isolator is constructed. To validate the accuracy, dynamic experiments are conducted on the isolator at distinct frequencies (6-9 Hz) and amplitudes (0.6-0.9 mm). The results indicate that the hysteresis dynamic model constructed based on the INCF model exhibits a remarkably high level of precision compared to the classic model (R2=0.998). This increased accuracy is attributed to the consideration of influencing factors of the INCF model, such as micro-collisions between spherical joints and interfacial friction during the operation of the isolator. These variables are determined by the material properties and geometric dimensions of the spherical joint and can be adjusted in real time based on the isolator deformations to enhance the model's accuracy. The method of parameter identification applied to overall structure resolves challenge of measuring the internal deformation of spherical joints. Importantly, the INCF model is not limited to the isolators proposed in this work but can also be applied to similar isolators with Stewart structure-type connections that employ spherical joints. These research findings provide a robust theoretical support for the design and performance optimization of the isolator, with the potential to positively impact related engineering applications.

Fast Terminal Synergetic Speed Control for SMPMSM Drive System

For the surface‐mounted permanent magnet synchronous motor (SMPMSM) drive system, a fast terminal synergetic speed control (FTSSC) is proposed to improve its speed dynamic performance and robustness. In the proposed control scheme, the ultra‐local model of SMPMSM drive system, which considers motor parameter uncertainties and unknown disturbances, is set up based on the algebraic parameter identification method. Then, the nonlinear terminal manifold is designed to achieve speed rapid convergence in finite time, moreover, the fast synergetic speed control law that satisfies the voltage and current constraints of SMPMSM drive system is derived. Meanwhile, the Lyapunov function is designed to prove the stability of the proposed fast terminal synergetic speed‐controlled SMPMSM drive system. Finally, the effectiveness and feasibility of proposed control is verified through the experimental research of SMPMSM drive system. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

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.

Novel Parameter Identification Method of Extended Debye Model for Long-Length Submarine Cables

Novel Parameter Identification Method of Extended Debye Model for Long-Length Submarine Cables

Determination of the Gurson-Tvergaard-Needleman damage model parameters for simulating small punch tests of heat-resistant alloys

The small punch (SP) test is utilized to assess the mechanical characteristics and damage progression of heat-resistant alloys. The inverse finite element analysis method incorporating SP tests is a parameter identification method based on adjusting the accuracy of the simulated load-displacement curves. In this paper, the elastoplastic parameters of the Hollomon model and the damage parameters of the Gurson-Tvergaard-Needleman (GTN) model are determined based on the undamaged and damaged stages of the load-displacement curves, respectively. The whole stress-strain curves of the tested materials are then built using the results of finite element simulations of the tensile specimens of ZG15Cr2Mo1, P91, 316H, and Hastelloy X at room and elevated temperatures. Comparison with uniaxial tensile tests indicates that the simulated stress-strain curves closely resemble the experimental data from the tensile testing. In addition, the simulated damage evolution characteristics of the SP specimens are consistent with the mechanical model based on the actual deformation behavior. It is possible to comprehend the damage evolution process by analyzing the SP specimens’ stress and strain change characteristics.

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.