Parameter Identification Method Research Articles

Closed-loop control strategy design for Caputo–Fabrizio definition-based fractional-order Boost converters considering parametric uncertainties

This paper proposes a dual closed-loop adaptive PI control system based on parameter identification for Caputo–Fabrizio (C-F) definition-based fractional-order boost converters. First, a C-F definition-based fractional-order model of the Boost converter is derived. Afterward, an online component parameter identification method is proposed to obtain the values and orders of capacitors and inductors in the proposed model. Based on these identified parameters, a dual closed-loop adaptive PI control system considering parametric uncertainties for the Boost converters is designed using the frequency domain analysis method. Finally, simulation validations are conducted. The online parameter identification method can accurately identify the values and orders of capacitors and inductors. The identification error is less than [Formula: see text] for the capacitance value and [Formula: see text] for the other parameters. Moreover, the proposed control system can automatically adjust the parameters with changes in the component parameters, effectively improving the robustness. Compared to traditional PI controllers, when the parameters of components change, the proposed control system can reduce voltage overshoot by [Formula: see text] in the case of sudden changes in the input voltage. These results confirm the efficacy of the proposed online parameter identification method and the dual closed-loop adaptive PI control system.

Line-parameter identification of medium-voltage distribution systems based on deep deterministic policy gradients

Accurate line-parameter identification is an important foundation for refined the regulation, protection, and control of distribution systems. Traditional identification models provide accurate modeling, while conventional identification approaches are hindered by the high complexity and low observability of power systems. In this article, a parameter identification method based on the deep deterministic policy gradient is proposed for medium voltage distribution systems. The proposed method starts with objective function constructing, followed by power flow analysis and parameter identification modeling, where the L2 normalization theory is introduced to improve the computation efficiency. On this basis, the parameter identification framework is constructed through designing the Markov decision process of a parameter and using a training mechanism. An adaptive parameter correction method is proposed to improve the accuracy and efficiency of a deep-reinforcement-learning-based agent. The performance of the proposed modal is tested on IEEE 14-node and IEEE 33-node medium-voltage distribution systems. Case simulation results demonstrate that the proposed modal exhibits superior computational capability, while achieving fewer errors compared to traditional methods.

Modeling Thermal Impedance of IGBT Devices Based on Fractional Calculus Techniques

The thermal impedance characteristics of insulated gate bipolar transistor (IGBT) modules are critical for the thermal management and design of electronic devices. This paper proposes a fractional-order equivalent thermal impedance model, which is inspired by the correlation between multi-time-scale dissipation characteristics of heat conduction processes and fractional calculus. The fractional-order equivalent thermal impedance model is derived based on the connection between fractional-order calculus and the Foster thermal network model in mathematical operations, with only two parameters to be identified: heat capacity C and fractional order α. Moreover, this paper provides a parameter identification method for the proposed fractional-order equivalent thermal impedance model based on the multi-objective particle swarm optimization (MOPSO) algorithm. In order to validate the effectiveness and superiority of this work, experiments and comparative works are provided in this paper. The results indicate that the fractional-order equivalent thermal impedance model can accurately describe the frequency domain characteristic curves of the thermal impedance of the Foster thermal network model for IGBT modules, with the difference between the amplitude frequency characteristics not exceeding 1 dB and the difference between the phase frequency characteristics not exceeding 1° within the operating frequency range of (1 kHz, 1 MHz).

Combined Identification of Vehicle Parameters and Road Surface Roughness Using Vehicle Responses

Highways, urban roads, and bridges are the important transportation infrastructures for the economic development of modern society. The evaluation of bridge and road quality is crucial to the maintenance and management of the bridge and road industry. Road roughness is a widely accepted indicator in the evaluation of road quality and safety, which is a major input source for vehicles. The vehicle responses-based method of identifying road roughness is efficient and convenient. However, the dynamic characteristics of the vehicle have an important impact on the interaction between the vehicle and the road. When the vehicle parameters are not yet clear, the coupling of unknown parameters and unknown road roughness results in the need for mutual iteration when the existing methods simultaneously identify vehicle parameters and road roughness. To address this issue, this study proposes an effective method for the combined identification of vehicle parameters and road roughness using vehicle responses. The test vehicle is modeled as a four-degree-of-freedom half-vehicle model. In view of the coupling effect between tire stiffness and road roughness, the unknown vehicle physical parameters, except for tire stiffness, are first included in the extended state vector. Based on the extended Kalman filter for unknown excitation (EKF-UI), unknown vehicle physical parameters and unknown forces on the axle are identified. Subsequently, based on the property that the front and rear axles of the vehicle pass through the same road roughness area at a fixed time lag, the tire stiffness is identified by combining the identified unknown forces on the axle. Finally, the road roughness is obtained using the identified vehicle parameters and unknown forces. Numerical studies with different levels of roughness, different noise levels, and different vehicle speeds have verified the accuracy of this method in identifying vehicle parameters and road roughness.

A novel friction coefficient model parameters identification method for needle-tissue insertion procedure

Abstract In endoscopic liver vascular insertion surgeries, during the process of angiographic operation, the success of vascular staining depends on precise needle insertion control which heavily relies on experienced surgeons. Endoscopic vascular insertion surgical navigation system shows the potential to improve position precision, however it relies on needle-tissue interaction model and parameter identification to provide essential information for improving needle insertion accuracy, in which the friction coefficient is important parameters but difficult to determine. In this paper, a novel needle-tissue friction coefficient identification method was proposed with unknown tissue Young's modulus under endoscopic liver surgery scenarios. A modified friction coefficient model was proposed including the adhesion and elastic friction component to describe needle-tissue dynamic interaction process which can predict the friction coefficient more precisely. The proposed parameter estimation method based on the modified friction model can simultaneously estimate friction coefficient and Young's modulus. The proposed method was demonstrated by the friction coefficient measurement experiment. The results showed that the friction coefficient model prediction results agreed well with expected value. The proposed method can be applied to provide essential tissue-needle interactive information to improve needle insertion precision in the endoscopic liver vascular insertion surgery scenarios.

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.

The Next Generation of Energy Intelligence: the Tactical Level

This article analyzes research dedicated to energy intelligence and proposes a definition of this concept as the process of systematically gathering and analyzing information about the energy resources, markets, policies, and technologies of countries. This process ena-bles the assessment of their economic and security impacts on a global (strategic) level. The article describes the purpose and primary objectives of energy intelligence, which in-volve the systematic collection and analysis of information about energy resources, mar-kets, and policies to understand their impact on business decisions, economic factors, and security aspects through a deep analysis of energy trends and geopolitical risks. The clas-sification of energy intelligence is provided, highlighting its main types. The article de-scribes the methods of energy intelligence (SIGINT, IMINT, OSINT, HUMINT, EX-IMINT) used for collecting and analyzing information about energy systems and re-sources. The subtypes of military energy intelligence are examined separately, emphasiz-ing their role in military conflicts. An important aspect of the article is the development of a method for parametric identification of military targets based on energy consumption values, which allows for more precise identification and evaluation of military targets in modern combat scenarios. Thus, the article provides a comprehensive overview of the top-ic, emphasizing the importance of an integrated approach and the involvement of various stakeholders to ensure effective energy intelligence.

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