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This study introduces a novel adjustment to the firefly algorithm (FA) through the integration of rare instances of cannibalism among fireflies, culminating in the development of the honeybee mating-based firefly algorithm (HBMFA). The IEEE Congress on Evolutionary Computation (CEC) 2005 benchmark functions served as a rigorous testing ground to evaluate the efficacy of the new algorithm in diverse optimization scenarios. Moreover, thorough statistical analyses, including two-sample t-tests and fitness function evaluation analysis, the algorithm’s optimization capabilities were robustly validated. Additionally, the coefficient of determination, used as an objective function, was utilized with real-world wind speed data from the SR-25 station in Brazil to assess the algorithm’s applicability in modeling wind speed parameters. Notably, HBMFA achieved superior solution accuracy, with enhancements averaging 0.025% compared to conventional FA, despite a moderate increase in execution time of approximately 18.74%. Furthermore, this dominance persisted when the algorithm’s performance was compared with other common optimization algorithms. However, some limitations exist, including the longer execution time of HBMFA, raising concerns about its practical applicability in scenarios where computational efficiency is critical. Additionally, while the new algorithm demonstrates improvements in fitness values, establishing the statistical significance of these differences compared to FA is not consistently achieved, which warrants further investigation. Nevertheless, the added value of this work lies in advancing the state-of-the-art in optimization algorithms, particularly in enhancing solution accuracy for critical engineering applications.

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 Consequently, classical beam theory is no longer applicable.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.

This paper addresses performance degradation in nonlinear flux linkage algorithms, arising from estimation errors in rotor flux linkage due to fluctuations in current and temperature. We introduce a parameter-adaptive flux linkage model using MRAS, which dynamically adjusts the rotor flux linkage, significantly minimizing estimation errors and improving control performance. When the rotor flux linkage of the motor undergoes sudden changes, the nonlinear flux linkage model shows a speed fluctuation of 5%, whereas the parameter-adaptive flux linkage model reduces the error to 0.6%. The algorithm demonstrates strong robustness when the stator resistance undergoes a sudden change. The control effectiveness under conditions such as load start and load mutation is excellent. Simulations and experiments demonstrate that the parameter-adaptive flux linkage model improves the estimation accuracy of the rotor position when motor parameters change.

Utilizing thermal updrafts shows potential for enabling long-endurance cruising of fixed-wing unmanned aerial vehicles without energy consumption. This article presents a novel online method based on sparse identification of nonlinear dynamics (SINDy) approach to achievement identification of thermal sources in the atmosphere. Initially, the algorithm is incorporated into the upper-level planning system, interacting with the lower-level controller. Then, experiments are conducted through software-in-the-loop simulations (SITL) to validate the implementation of the proposed algorithm. It is found that direct observation of thermal sources through measurements using SINDy is unfeasible during straight and circular flight modes. Nevertheless, simulation analysis of the proposed approach indicates that under unobservable conditions, a portion of the parameters can still be identified. By comparing results obtained using the particle filter algorithm, this method is shown to accurately estimate the parameters with negligible errors under observability conditions. The novelty of this approach lies in its significant improvement of the localization accuracy of the thermal source, without the need for parameter adjustments in the algorithm. Finally, the proposed methods are integrated into commonly used hardware platforms, and their online feasibility is verified through hardware-in-the-loop simulations.

The measurement of deep water gravity wave elevations using in situ devices, such as wave gauges, typically yields spatially sparse data due to the deployment of a limited number of costly devices. This sparsity complicates the reconstruction of the spatio-temporal extent of surface elevation and presents an ill-posed data assimilation problem, which is challenging to solve with conventional numerical techniques. To address this issue, we propose the application of a physics-informed neural network (PINN) to reconstruct physically consistent wave fields between two elevation time series measured at distinct locations within a numerical wave tank. Our method ensures this physical consistency by integrating residuals of the hydrodynamic nonlinear Schrödinger equation (NLSE) into the PINN’s loss function. We first showcase a data assimilation task by employing constant NLSE coefficients predetermined from spectral wave properties. However, due to the relatively short duration of these measurements and their possible deviation from the narrow-band assumptions inherent in the NLSE, using constant coefficients occasionally leads to poor reconstructions. To enhance this reconstruction quality, we introduce the base variables of frequency and wavenumber, from which the NLSE coefficients are determined, as additional neural network parameters that are fine tuned during PINN training. Overall, the results demonstrate the potential for real-world applications of the PINN method and represent a step toward improving the initialization of deterministic wave prediction methods.

The paper presents an approach to solving the problem of unknown motion parameters Bayesian identification for the stochastic dynamic system model with randomly delayed observations. The system identification and the object tracking tasks obtain solutions in the form of recurrent Bayesian relations for a posteriori probability density. These relations are not practically applicable due to the computational challenges they present. For practical implementation, we propose a conditionally minimax nonlinear filter that implements the concept of conditionally optimal estimation. The random delays model source is the area of autonomous underwater vehicle control. The paper discusses in detail a computational experiment based on a model that is closely aligned with this practical need. The discussion includes both a description of the filter synthesis features based on the geometric interpretation of the simulated measurements and an impact analysis of the effectiveness of model special factors, such as time delays and model unknown parameters. Furthermore, the paper puts forth a novel approach to the identification problem statement, positing a random jumping change in the motion parameters values.

The red-billed blue magpie optimizer (RBMO) is employed in this research study to address parameter extraction in polymer exchange membrane fuel cells (PEMFCs), along with three recently implemented optimizers. The sum of squared deviations (SSD) between the simulated and measured stack voltages defines the fitness function of the optimization problem under investigation subject to a set of working constraints. Three distinct PEMFCs stacks models—the Ballard Mark, Temasek 1 kW, and Horizon H-12 units—are used to illustrate the applied RBMO’s feasibility in solving this challenge in comparison to other recent algorithms. The highest percentages of biased voltage per reading for the Ballard Mark V, Temasek 1 kW, and Horizon H-12 are, respectively, +0.65%, +0.20%, and −0.14%, which are negligible errors. The primary characteristics of PEMFC stacks under changing reactant pressures and cell temperatures are used to evaluate the precision of the cropped optimized parameters. In the final phase of this endeavor, the sensitivity of the cropped parameters to the PEMFCs model’s performance is investigated using two machine learning techniques, namely, artificial neural network and Gaussian process regression models. The simulation results demonstrate that the RBMO approach extracts the PEMFCs’ appropriate parameters with high precision.

The increasing adoption of batteries in a variety of applications has highlighted the necessity of accurate parameter identification and effective modeling, especially for lithium-ion batteries, which are preferred due to their high power and energy densities. This paper proposes a comprehensive framework using the Levenberg–Marquardt algorithm (LMA) for validating and identifying lithium-ion battery model parameters to improve the accuracy of state of charge (SOC) estimations, using only discharging measurements in the N-order Thevenin equivalent circuit model, thereby increasing computational efficiency. The framework encompasses two key stages: model parameter identification and model verification. This framework is validated using experimental measurements on the INR 18650-20R battery, produced by Samsung SDI Co., Ltd. (Suwon, Republic of Korea), conducted by the Center for Advanced Life Cycle Engineering (CALCE) battery group at the University of Maryland. The proposed framework demonstrates robustness and accuracy. The results indicate that optimization using only the discharging data suffices for accurate parameter estimation. In addition, it demonstrates excellent agreement with the experimental measurements. The research underscores the effectiveness of the proposed framework in enhancing SOC estimation accuracy, thus contributing significantly to the reliable performance and longevity of lithium-ion batteries in practical applications.

The study proposes an active trailer steering control method for tractor semitrailers to promote the path tracking effect of the trailer portion as well as lateral stability during lane changing. Firstly, a simplified model of a tractor semitrailer is constructed, and the MAP map is formed based on the genetic algorithm for the identification of the key parameters, which improves the model’s accuracy. Then the tractor and trailer’s yaw rate and sideslip angle at CG are tracked as the control objective and the trailer angle distribution strategy is given. Then the LQR-based corner controller is designed to control the steering actuators of each axle of the trailer. Finally, the effectiveness of the designed control strategy is verified based on the Trucksim/Simulink joint simulation platform and the semi-physical HiL test platform. The simulation results show that the designed controller can effectively improve the path tracking effect of the tractor and the trailer, and at the same time, the lateral stability parameters of the tractor and the trailer are also significantly improved, which improves the driving stability of the tractor semitrailer.

Subsynchronous oscillation (SSO) is the resonance between a new energy generator set and a weak power grid, and the resonance frequency is usually the sub-/super-synchronous frequency. The eigensystem realization algorithm (ERA) is a classic algorithm for extracting modal parameters based on matrix decomposition. By leveraging the ERA’s simplicity and low computational cost, an enhanced methodology for identifying the key parameters of SSO is introduced. The enhanced algorithm realizes SSO angular frequency extraction by constructing an angular frequency fitting equation, enabling efficient identification of SSO parameters using only a 200 ms synchrophasor sequence. In the process of identification, the fitting-based ERA effectively addresses the limitation of the existing ERA. The accuracy of SSO parameter identification is improved, thereby realizing that SSO parameter identification can be carried out using a 200 ms data window. The fitting-based ERA is verified using synthetic and actual data from synchrophasor measurement terminals. The research results show that the proposed algorithm can accurately extract fundamental and subsynchronous or supersynchronous oscillation parameters, effectively realizing dynamic real-time monitoring of subsynchronous oscillations.

Accurate estimation of State-of-Charge (SoC) is essential for ensuring the safe and efficient operation of electric vehicles (EVs). Currently, second-order RC equivalent circuit models do not account for the influence of battery charging and discharging states on battery parameters. Additionally, offline parameter identification becomes inaccurate as the battery ages. Online identification requires real-time parameter updates during the SoC estimation process, which increases the computational complexity and reduces the computational efficiency of real vehicle Battery Management System (BMS) chips. To address these issues, this paper proposes a SoC estimation method that combines online and offline identification based on an optimized second-order RC equivalent circuit model, which distinguishes it from existing methods in the field. On the basis of the traditional second-order RC model, the Ohmic resistance (R0), polarization resistance (R1), polarization capacitance (C1), diffusion resistance (R2), and diffusion capacitance (C2) during the charging and discharging processes are discussed separately. R0, which does not change frequently, is identified offline, while R1, R2, C1, and C2, which dynamically change with time and current, are identified online. To thoroughly verify the feasibility of the proposed method, we construct an SoC estimation test bench, which allows us to adjust the battery’s surface temperature in real time using a temperature control chamber. Experimental validation under Federal Urban Driving Schedule (FUDS) (−10 °C to 45 °C, 80% battery capacity) and Dynamic Stress Test (DST) (−10 °C to 45 °C, 8% battery capacity) conditions demonstrate that our method improves SoC estimation accuracy by 16.28% under FUDS and 28.2% under DST compared to the improved GRU-based transfer learning method, while maintaining system SoC estimation efficiency.

This research focused on the rubber bushings of the rear sub-frame in an electric vehicle. A dynamic model was developed to represent the bushing, incorporating an elastic element, a frictional element, and a viscoelastic element arranged in series using a fractional-order Maxwell and a Kelvin–Voigt model. To identify the parameters of the bushing model, an improved adaptive chaotic particle swarm optimization algorithm was employed, in conjunction with dynamic stiffness test data obtained at an amplitude of 0.2 mm. The test data obtained at different amplitudes (0.2 mm, 0.3 mm, 0.5 mm, and 1 mm) were fitted to the model, resulting in fitting errors of 1.13%, 4.07%, 4.42%, and 28.82%, respectively, when compared to the corresponding test data in order to enhance the accuracy of the model fitting; the Sobol sensitivity analysis method was utilized to analyze the parameter sensitivity of the model. Following the analysis, the parameters α, β, and k2, which exhibited high sensitivity, were re-identified. This re-identification process led to a reduction in the fitting error at the 1 mm amplitude to 7.45%. The improved accuracy of the model plays a crucial role in enhancing the simulation accuracy of design of experiments (DOE) analysis and verifying the vehicle’s performance under various conditions, taking into account the influence of the bushing.

Researchers tackled the challenge of finding the right parameters for a transformer-equivalent circuit. They achieved this by minimizing the difference between actual measurements (currents, powers, secondary voltage) during a transformer load test and the values predicted by the model using different parameter settings. This process considers limitations on what values the parameters can have. This research introduces the application of a new and effective optimization algorithm called the dandelion algorithm (DA) to determine these transformer parameters. Information from real-time tests (single- and three-phase transformers) is fed into a computer program that uses the DA to find the best parameters by minimizing the aforementioned difference. Tests confirm that the DA is a reliable and accurate tool for estimating the transformer parameters. It achieves excellent performance and stability in finding the optimal values that precisely reflect how a transformer behaves. The DA achieved a significantly lower best fitness function value of 0.0136101 for the three-phase transformer case, while for the single-phase case it reached 0.601764. This indicates a substantially improved match between estimated and measured electrical parameters for the three-phase transformer model. By comparing DA with six competitive algorithms to prove how well each method minimized the difference between measurements and predictions, it could be shown that the DA outperforms these other techniques.

A new optimization method for estimating the State of Charge (SOC) of battery charge state is proposed. This method incorporates the Levenberg–Marquardt Algorithm (LMA) for online parameter identification and the Extended Kalman Filter (EKF) for SOC. On the one hand, the LMA efficiently alleviates the ’Data saturation’ problem experienced by least squares methods by dynamically adjusting weights of data. On the other hand, the EKF improves the robustness and adaptability of SOC estimation. Simulation results under Hybrid Pulse Power Characteristic (HPPC) conditions demonstrate that this new approach offers superior performance in SOC estimation in batteries for electric vehicles compared to existing methods, with better tracking of the true SOC curve, reduced estimation error, and improved convergence.

With the large-scale integration of wind power, it is essential to establish an electromagnetic transient (EMT) model of a wind turbine system. Focusing on the problem of the difficulty in obtaining the parameters of the direct-driven permanent magnet synchronous generator (PMSG) model, this manuscript proposes a method based on trajectory sensitivity analysis and improved gray wolf optimization (IGWO) for identifying the parameters of the PMSG EMT model. First, a model of a PMSG wind turbine is established on an EMT simulation platform. Then, the key parameters of the model are determined based on the sensitivity analysis. Five control parameters are selected as the key parameters for their higher sensitivity indexes. Finally, the key parameters are accurately identified, using the proposed IGWO algorithm. The final case study demonstrates that the proposed IGWO algorithm has better optimization performance compared with the GWO algorithm and particle swarm optimization (PSO) algorithm. In addition, the simulation waveforms show that the identified parameters are accurate and applicable to other operating conditions.

Magnetorheological dampers (MRDs) are of great interest in engineering due to their continuously adjustable damping characteristics. Accurate models are essential for optimizing MRDs and analyzing system dynamics. However, conventional methods widely overlook the impact of excitation frequency and amplitude. To address this issue, this work proposes a modified Bouc–Wen model that can be adapted to various excitation conditions. The model’s parameters depend on the current, excitation frequency, and amplitude. The mechanical characteristics of the MRD were analyzed by the tests. The parameters in the Bouc–Wen model were identified by combining the harmonic balance method and the genetic algorithm. The modified Bouc–Wen model was established by analyzing the variation of each parameter with current, excitation frequency, and amplitude. Finally, the agreement between the modified prediction model and the test results was verified under sinusoidal excitation of 80 mm and 1 Hz. The average relative errors were 3.87%, 2.82%, 2.45%, 2.19%, and 3.27% for current excitations of 0 A, 0.5 A, 1 A, 1.5 A, and 2.0 A, respectively. Since the MRD in this paper operates from 0.5 Hz to 2 Hz, the modified model was validated in the same range. Experiments demonstrate that the modified Bouc–Wen model efficiently and accurately describes the mechanical properties of the MRD under various excitation conditions.

This research aims to propose a novel approach for evaluating and minimizing scraps in an industrial production of premium food cans with distortion printing. Beyond conventional formability criteria, a waving requirement is introduced to ensure aesthetic quality of the printed graphics. The research focuses on real production conditions, specifically involving double-cold-reduced (DR) low-carbon steel sheets and chromium-coated tin-free steel with a thickness of 0.16 mm. The sheets are laminated on both sides with a plastic film prior to undergoing distortion printing on the exterior. Subsequently, a blank is subjected to a drawing-redrawing process to form a food can. To address challenges associated with characterizing these thin sheets, a material parameter identification method is proposed and demonstrated. The thickness profile and flange length are identified as key criteria for this identification process. Measurements of thickness distribution and flange length are obtained using digital image correlation (DIC) and microscopy techniques. Within the manufacturing system, uncertainties related to material properties and forming processes can result in scraps or defects. To analyze these processes, finite element analysis (FEA) is employed and validated through experiments. For the evaluation of scrap rates, uncertainty propagation is conducted using a metamodeling technique, specifically employing radial basis function (RBF) neural networks. The study concludes by offering process optimization recommendations aimed at reducing the scrap rate.

Vibration problems have become one of the most important factors affecting robot performance. To this end, a terminal residual vibration suppression method based on joint trajectory optimization is proposed to improve the accuracy and stability of robot motion. Firstly, based on the characteristics of the friction nonlinearity due to joint coupling and physical feasibility of dynamic parameters, a semidefinite programming method is used to identify dynamic parameters with actual physical meaning, thereby obtaining an accurate dynamic model. Then, based on the result of the residual vibration time domain analysis, a joint trajectory optimization model with the goal of minimizing joint tracking error is established. The Chebyshev collocation method is used to discretize the optimization model. The dynamic model is used as the optimization constraint, and barycentric interpolation is used to obtain the optimized joint motion trajectory. Finally, industrial robot experiments prove that the vibration suppression method proposed in this article can reduce the maximum acceleration amplitude of residual vibration by 62% and the vibration duration by 71%. Compared with the input shaping method, the method proposed in this paper can reduce the terminal residual vibration more effectively and ensure the consistency of running time and trajectory.

As the market for hybrid and electric vehicles expands, electric motor production and testing technology must be continuously improved to meet the cost and quality requirements of mass production. In order to detect faults in motors during the production process, a condition monitoring tool is used for the motor end line. During most condition monitoring, the motor operates in a static state where the speed of the motor remains constant and the voltage/current is recorded for a certain period. This process usually takes a long time and requires a loader to drag the motor to a standstill at a constant speed. In this paper, various transient process testing methods are introduced. For these processes, only transient operation of the motor, such as acceleration, loss, or a short circuit, is required. By analyzing the measurement results and simulation results of motor models, unhealthy motors can be detected more effectively.

Similar to bulk metal forming, clinch joining is characterised by large plastic deformations and a variety of different 3D stress states, including severe compression. However, inherent to plastic forming is the nucleation and growth of defects, whose detrimental effects on the material behaviour can be described by continuum damage models and eventually lead to material failure. As the damage evolution strongly depends on the stress state, a stress-state-dependent model is utilised to correctly track the accumulation. To formulate and parameterise this model, besides classical experiments, so-called modified punch tests are also integrated herein to enhance the calibration of the failure model by capturing a larger range of stress states and metal-forming-specific loading conditions. Moreover, when highly ductile materials are considered, such as the dual-phase steel HCT590X and the aluminium alloy EN AW-6014 T4 investigated here, strong necking and localisation might occur prior to fracture. This can alter the stress state and affect the actual strain at failure. This influence is captured by coupling plasticity and damage to incorporate the damage-induced softening effect. Its relative importance is shown by conducting inverse parameter identifications to determine damage and failure parameters for both mentioned ductile metals based on up to 12 different experiments.

The problem of the insufficient accuracy performance of industrial robots in high-precision manufacturing is addressed in this paper. Firstly, a kinematic error model based on an M-DH model was presented. Secondly, a hybrid observability index O6 was proposed to select the optimal poses for parameter identification. O6 is the combination of O1 and O3. The optimal poses were obtained by using the IOOPS algorithm. Thirdly, the fitness function for parameter identification was established, and the Levenberg–Marquardt (LM) algorithm was applied for the accurate identification of kinematic parameter errors. Finally, several experiments were conducted to evaluate the performance of the proposed hybrid observability index O6. The average position error and average attitude error of Staubli TX60 robot were reduced by 89% and 49%. The results show that the proposed hybrid observability index O6 has great stability and effectiveness for robot calibration.

Online condition-monitoring techniques that are used to reveal incipient faults before breakdowns occur are typically data-driven or model-based. We propose the use of a fundamental physics-based thermofluid model of a heat pump cycle combined with deep learning-based surrogate models and parameter identification in order to simultaneously detect, locate, and quantify degradation occurring in the different components. The methodology is demonstrated with the aid of synthetically generated data, which include the effect of measurement uncertainty. A “forward” neural network surrogate model is trained and then combined with parameter identification which minimizes the residuals between the surrogate model results and the measured plant data. For the forward approach using four measured performance parameters with 100 or more measured data points, very good prediction accuracy is achieved, even with as much as 20% noise imposed on the measured data. Very good accuracy is also achieved with as few as 10 measured data points with noise up to 5%. However, prediction accuracy is reduced with less data points and more measurement uncertainty. A “backward” neural network surrogate model can also be applied directly without parameter identification and is therefore much faster. However, it is more challenging to train and produce less accurate predictions. The forward approach is fast enough so that the calculation time does not impede its application in practice, and it can still be applied if some of the measured performance parameters are no longer available, due to sensor failure for instance, albeit with reduced accuracy.

Predicting the system efficiency of green energy and developing forward-looking power technologies are key points to accelerating the global energy transition. This research focuses on optimizing the parameters of proton exchange membrane fuel cells (PEMFCs) and photovoltaic (PV) cells using the honey badger algorithm (HBA), a swarm intelligence algorithm, to accurately present the performance characteristics and efficiency of the systems. Although the HBA has a fast search speed, it was found that the algorithm’s search stability is relatively low. Therefore, this study also enhances the HBA’s global search capability through the rapid iterative characteristics of spiral search. This method will effectively expand the algorithm’s functional search range in a multidimensional and complex solution space. Additionally, the introduction of a sigmoid function will smoothen the algorithm’s exploration and exploitation mechanisms. To test the robustness of the proposed methodology, an extensive test was conducted using the CEC’17 benchmark functions set and real-life applications of PEMFC and PV cells. The results of the aforementioned test proved that with regard to the optimization of PEMFC and PV cell parameters, the improved HBA is significantly advantageous to the original in terms of both solving capability and speed. The results of this research study not only make definite progress in the field of bio-inspired computing but, more importantly, provide a rapid and accurate method for predicting the maximum power point for fuel cells and photovoltaic cells, offering a more efficient and intelligent solution for green energy.

The spacecraft docking motion simulation system for on-orbit docking plays a very important role in some theoretical research and engineering application fields. The parallel robot utilized in the spacecraft docking simulation system requires high positioning and orientation accuracy to achieve better simulation results. A novel kinematic parameter identification method with an improved particle swarm optimization (PSO) algorithm is proposed to enhance positioning and orientation accuracy of the parallel robot. A fitness function is established using these residuals between the measured and computed poses by a coordinate measuring machine and forward kinematics. The kinematic parameter identification problem is turned into a high-dimensional nonlinear optimization in which the unknown kinematic parameter errors are regarded as optimal variables. The optimal variables are solved by the proposed improved PSO algorithm. The mean values of the positioning and orientation errors are reduced from 4.3268 mm and 0.2221 deg to 0.7692 mm and 0.0674 deg, respectively. The proposed kinematic parameter identification method increases the positioning accuracy mean by 22.26% and the orientation accuracy mean by 32.80% compared with the least squares method. The kinematic parameter identification method with the improved PSO algorithm can effectively enhance positioning and orientation accuracy of the parallel robot for docking motion simulation.

This paper addresses the further development and optimization of a modeling- and machine learning-based framework for early fault detection and diagnosis in induction motors. The goal behind the multi-level framework is to provide a pragmatic and practical approach for the autonomous monitoring of electrical machines in various industrial applications. The main contributions of this paper include the elimination of a fingerprint measurement in the processing of the framework and the development of a generalized model for fault detection and diagnosis. These aspects allow the training of neural networks with a simulated data set before even knowing the specific induction motor to be monitored. The pre-trained feed-forward neural networks enable the detection of several electrical and mechanical faults in a real induction motor with an overall accuracy of 99.56%. Another main contribution is the extension of the methodology to a larger operating range. As a result, various faults in a real induction motor can be detected under different load conditions with accuracies of over 92%. As a further part of the paper, a concept for a prototype is presented, which enables the autonomous and practice-friendly application of the framework.

The aim of this work is to optimize the sensor positions of a sensor–actuator measurement system for identifying local variations in the magnetic permeability of cut steel sheets. Before solving the actual identification problem, i.e., finding the material distribution, the sensor placement of the measurement setup should be improved in order to reduce the uncertainty of the identification of the material distribution. The Fisher information matrix (FIM), which allows one to quantify the amount of information that the measurements carry about the unknown parameters, is used as the main metric for the objective function of this design optimization. The forward problem is solved by the finite element method. The results show that the proposed method is able to find optimal sensor positions as well as the minimum number of sensors to achieve a desired maximum parameter uncertainty.

The aim of this study is to propose a damper based on magnetorheological (MR) fluid and elastomer for application in vehicle engine mounting systems to dissipate the vibration energy transferred from the engine to the vehicle body. The magnetic circuit structure of the damper has been precisely designed, and its reasonableness has been verified by static magnetic field simulation. After the principle prototype’s completion, the damper’s mechanical properties are tested by an electro–hydraulic servo fatigue machine. The results show that with the current increase, the damper’s in-phase stiffness increases by 20.6%. The equivalent damping improves by 81.6%, which indicates that the damper has a good MR effect. A new phenomenological model is proposed, and a genetic algorithm is used to identify the parameters of the model. Finally, a 1/4 vehicle engine vibration damping system model is established and a dynamics simulation is carried out. The simulation results show that the damper effectively reduces the vibration transmitted from the engine to the body, and the vibration-damping effect is even more obvious through sky-hook control. This proves that the damper proposed in this study has good vibration-damping performance.

The open-circuit voltage (OCV) curve has a significant influence on the accuracy of the state of charge (SOC) estimation based on equivalent circuit models (ECMs). However, OCV curves are tested through offline experiments and are hard to be very accurate because they constantly change with the test method’s ambient temperature and aging status. Recently, researchers have attempted to improve the accuracy of OCV curves by increasing the volume of sample data or updating/reconstructing the curve combined with practical operation data. Still, prior offline tests are essential, and experimental errors inevitably exist. Consequently, a SOC estimation method without any offline OCV tests might be an efficient route to improve the accuracy of SOC. According to this idea, this paper presents a novel method for SOC estimation, which is based on online OCV curve construction. Meanwhile, a stepwise multi-timescale parameter identification algorithm is designed to improve the interpretability and precision of the estimated ECM parameters. The results demonstrate that the maximum SOC estimation error is only 0.05% at 25 °C, indicating good robustness under various ambient temperatures and operational conditions.

This paper addresses the parameter identification of a one-link flexible manipulator based on the experimental measurement of the inputs/outputs, the finite element model, and the application of evolutionary algorithms. A novel approach is proposed to find the values of inertia, stiffness, and damping parameters by minimizing the difference between the numerical model’s outputs and the testbed’s outputs, thus considering the joint position and acceleration of the link’s tip. The dynamic model is initially obtained using the finite element method and the Lagrange principle. A prototype of a single one-link flexible manipulator is used in the experimental application, wherein the servomotor applies the input torque, and the outputs are the joint angle and the link’s tip acceleration. Then, an optimization problem minimizes the difference between the numerical and experimental outputs to determine the set of parameters using evolutionary algorithms. A comparative analysis to obtain the identified parameters is established using genetic algorithms, particle swarm optimization, and differential evolution. The proposed identification approach permitted the determination of the dynamic parameters based on the complete dynamic model of the flexible-link manipulator, which is different from the approaches reported in the literature that identify a simplified model. This information is essential for the design of the motion and vibration control laws.

Kinematic calibration plays a pivotal role in enhancing the absolute positioning accuracy of industrial robots, with parameter identification and error compensation constituting its core components. While the conventional parameter identification method, based on linearization, has shown promise, it suffers from the loss of high-order system information. To address this issue, we propose an unscented Kalman filter (UKF) with adaptive process noise covariance for robot kinematic parameter identification. The kinematic model of a typical 6-degree-of-freedom industrial robot is established. The UKF is introduced to identify the unknown constant parameters within this model. To mitigate the reliance of the UKF on the process noise covariance, an adaptive process noise covariance strategy is proposed to adjust and correct this covariance. The effectiveness of the proposed algorithm is then demonstrated through identification and error compensation experiments for the industrial robot. Results indicate its superior stability and accuracy across various initial conditions. Compared to the conventional UKF algorithm, the proposed approach enhances the robot’s accuracy stability by 25% under differing initial conditions. Moreover, compared to alternative methods such as the extended Kalman algorithm, particle swarm optimization algorithm, and grey wolf algorithm, the proposed approach yields average improvements of 4.13%, 26.47%, and 41.59%, respectively.

Gravity sand casting simulations require accurate modelling of heat transfer phenomena to reliably evaluate the expected quality of the produced parts. Average model parameters can be easily retrieved from a validated database. However, these parameters are highly dependent on the specific sand used and the actual forming process in the foundry. Furthermore, the heat transfer from the solidifying alloy to the mould surfaces is not precisely known, so simulation models usually use typical values for overall heat transfer coefficients. Most research works investigate individual parameters, whereas heat transfer phenomena largely arise from their interaction together. Therefore, the present work describes a combined experimental and computational method based on genetic algorithm techniques for determining the most important parameters for heat transfer in a sand mould. The experiments examine both virgin and reused sand, as these are alternatively used in the foundry for mould forming. The density, thermal conductivity, and specific heat capacity of the different sands are identified, along with heat transfer coefficients. The counterproof simulations demonstrate that the standard parameters are quite reliable for virgin sand. However, in the case of reused sand, the identified parameters lead to more reliable results.

With the advancement of network technology, there has been an increase in the volume of data being transmitted across networks. Due to the bandwidth limitation of communication channels, data often need to be quantized or event-triggered mechanisms are introduced to conserve communication resources. On the other hand, network uncertainty can lead to data loss and destroy data integrity. This paper investigates the identification of finite impulse response (FIR) systems under the framework of stochastic noise and the combined effects of the event-triggered mechanism and uncertain communications. The study provides a reference for the application of remote system identification under transmission-constrained and packet loss scenarios. First, a congruential summation-triggered communication scheme (CSTCS) is introduced to lower the communication rate. Then, parameter estimation algorithms are designed for scenarios with known and unknown packet loss probabilities, respectively, and their strong convergence is proved. Furthermore, an approximate expression for the convergence rate is obtained by data fitting under the condition of uncertain packet loss probability, treating the trade-off between convergence performance and communication resource usage as a constrained optimization problem. Finally, the rationality and correctness of the algorithm are verified by numerical simulations.

The main component of the hydrogen production system is the electrolyzer (EL), which is used to convert electrical energy and water into hydrogen and oxygen. The power converter supplies the EL, and the controller is used to ensure the global stability and safety of the overall system. This review aims to investigate and analyze each one of these components: Proton Exchange Membrane Electrolyzer (PEM EL) electrical modeling, DC/DC power converters, and control approaches. To achieve this desired result, a review of the literature survey and an investigation of the PEM EL electrical modeling of the empirical and semi-empirical, including the static and dynamic models, are carried out. In addition, other sub-models used to predict the temperature, gas flow rates (H2 and O2), hydrogen pressure, and energy efficiency for PEM EL are covered. DC/DC power converters suitable for PEM EL are discussed in terms of efficiency, current ripple, voltage ratio, and their ability to operate in the case of power switch failure. This review involves analysis and investigation of PEM EL control strategies and approaches previously used to achieve control objectives, robustness, and reliability in studying the DC/DC converter-PEM electrolyzer system. The paper also highlights the online parameter identification of the PEM electrolyzer model and adaptive control issues. Finally, a discussion of the results is developed to emphasize the strengths, weaknesses, and imperfections of the literature on this subject as well as proposing ideas and challenges for future work.

Lithium-ion batteries are widely used in electric vehicles and renewable energy storage systems due to their superior performance in most aspects. Battery parameter identification, as one of the core technologies to achieve an efficient battery management system (BMS), is the key to predicting and managing the performance of Li-ion batteries. However, due to the complex chemical reactions and thermodynamic processes inside lithium-ion batteries, coupled with the influence of the external environment, accurate identification of lithium-ion battery parameters has become an urgent problem to be solved. In addition, data-driven parameter identification can enable battery models to better understand battery behavior, which is one of the focuses of future research. For this reason, this paper comprehensively reviews the application of data-driven parameter identification methods in different scenarios. Firstly, the research briefly explains the working principle of lithium-ion batteries and the key parameters affecting their performance. Secondly, this paper deeply discusses data-driven methods for parameter identification, which are widely used nowadays, and provides improvement ideas to address the shortcomings of traditional methods. Finally, the paper discusses the challenges faced by parameter identification technology for lithium-ion batteries and envisages future prospects.

The subsynchronous oscillation caused by the resonance between power electronic devices and series compensation devices or weak power grids introduced by large-scale renewable energy generation greatly reduces the transmission capacity of the system and may endanger the safe operation of the power system. It even leads to system oscillation instability. In this paper, based on the advantages of a simple solution, a small amount of calculation and anti-noise of ERA, a method of subsynchronous oscillation parameter identification based on the eigensystem realization algorithm (ERA) is proposed. The Hankel matrix in the improved ERA is obtained by splicing the real part matrix and the imaginary part matrix of the synchrophasor, thus solving the problem of angular frequency conjugate constraints of two fundamental components and two oscillatory components which are not considered in the existing ERA. The solution to this problem is helpful to improve the accurate parameter identification results of ERA under the data window of 200 ms and weaken the limitation caused by the assumption that the synchrophasor model is fixed. The practicability of the improved method based on PMU is verified by the synthesis of ERA and the actual measurement data. Compared with the existing ERA, the improved ERA can accurately identify the parameters of each component under the ultra-short data window and realize the dynamic monitoring of power system subsynchronous oscillation.

Modeling and predicting the long-term performance of Li-ion batteries is crucial for the effective design and efficient operation of integrated energy systems. In this paper, we introduce a comprehensive semi-empirical model for Li-ion cells, capturing electrothermal and aging features. This model replicates the evolution of cell voltage, capacity, and internal resistance, in relation to the cell actual operating conditions, and estimates the ongoing degradation in capacity and internal resistance due to the battery use. Thus, the model articulates into two sub-models, an electrothermal one, describing the battery voltage, and an aging one, computing the ongoing degradation. We first propose an approach to identify the parameters of both sub-models. Then, we validate the identification procedure and the accuracy of the electrothermal and aging models through an experimental campaign, also comprising two real cycle load tests at different temperatures, in which real measurements collected from real Li-ion cells are used. The overall model demonstrates good performances in simulating battery characteristics and forecasting degradation. The results show a Mean Absolute Percentage Error (MAPE) lower than 1% for battery voltage and capacity, and a maximum absolute error on internal resistance that is on par with the most up-to-date empirical models. The proposed approach is therefore well-suited for implementation in system modeling, and can be employed as an informative tool for enhancing battery design and operational strategies.

The electrochemical proton exchange membrane fuel cell (PEMFC) is an electrical generator that utilizes a chemical reaction mechanism to produce electricity, serving as a sustainable and environmentally friendly energy source. To thoroughly analyze and develop the features and performance of a PEMFC, it is essential to use a precise model that incorporates exact parameters to effectively suit the polarization curve. In addition, parameter extraction plays a crucial role in the simulation analysis, evaluation, optimum control, and fault detection of the proton exchange membrane fuel cell (PEMFC) system. Despite the development of many algorithms for parameter extraction in PEMFC, obtaining accurate and trustworthy results rapidly remains a challenge. This study presents a hybridized algorithm, namely differential evolution ameliorated (DEA) for reliably estimating PEMFC model parameters. To evaluate the proposed DEA-based parameter identification, a comparison analysis with previously published methods is conducted using MATLAB/SimulinkTM (R2016b, MathWorks, Natick, MA, USA) in terms of system correctness and convergence process. The proposed DEA algorithm is tested to extract the parameters of two PEMFC models: SR-12 500 W and 250 W. The sum of the squared errors (SSE) between the experimental and the obtained voltage data is defined as an objective function. The simulation results prove that the suggested DEA algorithm is capable of identifying the optimal PEMFC parameters rapidly and accurately in comparison with other optimization algorithms.

The coronavirus disease 2019 (COVID-19) pandemic disrupted public health and economies worldwide. In this paper, we investigate an optimal control problem to simultaneously minimize the epidemic size and control costs associated with intervention strategies based on official data. Considering people with undetected infections, we establish a control system of COVID-19 with time-varying parameters. To estimate these parameters, a parameter identification scheme is adopted and a mixed algorithm is constructed. Moreover, we present an optimal control problem with two objectives that involve the newly increased number of infected individuals and the control costs. A numerical scheme is conducted, simulating the epidemic data pertaining to Shanghai during the period of 2022, caused by the Omicron variant. Coefficient combinations of the objectives are obtained, and the optimal control measures for different infection peaks are indicated. The numerical results suggest that the identification variables obtained by using the constructed mixed algorithm to solve the parameter identification problem are feasible. Optimal control measures for different epidemic peaks can serve as references for decision-makers.

This paper presents an adaptive dynamic surface sliding mode control technique to address the issue of system parameter changes in permanent magnet synchronous motor (PMSM) position servo systems. The proposed method involves adopting a linear parameter varying (LPV) observer-based parameter identification algorithm and adaptive control technique. Initially, a mathematical model of the PMSM is established, and the system parameters are divided into nominal and perturbation values. This allows for the reconstruction of the system model into a state space equation that incorporates the unknown perturbation parameters. To accurately estimate these unknown parameters, an LPV observer is designed based on the reconstructed model. Additionally, an adaptive dynamic surface sliding mode control technique is explored to achieve the desired tracking performance. Meanwhile, an exponential reaching law is introduced to expedite the dynamic behavior of the system and mitigate chattering. Finally, a suitable Lyapunov function is selected to ensure the overall stability of the system. The simulation results demonstrate the effectiveness of the parameter identification and control algorithm in achieving good identification and tracking control ability for PMSM systems.

This paper addresses the problem of parameter identification for discrete-time stochastic systems with unknown exogenous inputs. These systems form an important class of dynamic stochastic system models used to describe objects and processes under a high level of a priori uncertainty, when it is not possible to make any assumptions about the evolution of the unknown input signal or its statistical properties. The main purpose of this paper is to construct a new SVD-based modification of the existing Gillijns and De Moor filtering algorithm for linear discrete-time stochastic systems with unknown exogenous inputs. Using the theoretical results obtained, we demonstrate how this modified algorithm can be applied to solve the problem of parameter identification. The results of our numerical experiments conducted in MATLAB confirm the effectiveness of the SVD-based parameter identification method that was developed, under conditions of unknown exogenous inputs, compared to maximum likelihood parameter identification when exogenous inputs are known.

The current lack of a high-precision, real-time model applicable to the control optimization process of heat exchange systems, especially the difficulty in determining the overall heat transfer coefficient K of heat exchanger operating parameters in real time, is a prominent issue. This paper mainly unfolds the following work: 1. We propose a dynamic model for the control and optimization of the heat exchanger operation. By constructing a system to collect real-time operating data on the flow rates and temperatures on both sides of the heat exchanger, the parameter identification of the overall heat transfer coefficient K is performed. Subsequently, by combining this with mechanistic equations, a novel heat exchanger model is established based on the fusion of mechanistic principles and reinforcement learning. 2. We validate the new model, where the average relative error between the model’s temperature output values and the actual measured values is below 5%, indicating the high identification accuracy of the model. Moreover, under variations in the temperature and flow rate, the overall heat transfer coefficient K demonstrates the correct patterns of change. 3. To further enhance the model’s identification accuracy, a study on the reward functions in reinforcement learning is conducted. A model with the Logarithmic Mean Temperature Difference (LMTD) as the reward function exhibits a high identification accuracy. However, upon comparison, a model using the Arithmetic Mean Temperature Difference (AMTD) for relative error as the reward function shows an even higher identification accuracy. The model is validated under various operating conditions, such as changes in the flow rate on the hot side, demonstrating good scalability and applicability. This research contributes to providing a high-precision dynamic parameter basis for the precise control of heat exchange systems, offering significant guidance for the control optimization of actual heat exchange system operations.

Identifying the parameters of multispan rigid frames is challenging because of their complex structures and large computational workloads. This paper presents a stiffness separation method for the static response parameter identification of multispan rigid frames. The stiffness separation method segments the global stiffness matrix of the overall structure into the stiffness matrices of its substructures, which are to be computed, thereby reducing the computational workload and improving the efficiency of parameter identification. Loads can be applied individually to each separate substructure, thereby guaranteeing obvious local static responses. The veracity and efficacy of the proposed methodology are substantiated by applying it to three- and eight-span continuous rigid frame structures. The findings indicate that the proposed approach significantly enhances the efficiency of parameter identification for multispan rigid frames.

Inspired by the difference in the friction radii of the pads from the high-speed train brake system, stick–slip experiments for a disc–block friction system with different friction radii were carried out via a test device. Based on the test results, the stick–slip vibration characteristics of the disc–block friction system with variation in the friction radius were analyzed, and the corresponding Stribeck model parameters in exponential and fractional forms were identified. The experimental results show that with an increase in the friction radius the vibration amplitude first increased and then decreased and the frequency of stick–slip vibration increased. The identified Stribeck model parameters show that the decay factors increased, the static friction coefficient decreased, and the dynamic friction coefficient decreased first and then increased as the friction radius increased. Moreover, the identified Stribeck model in an exponential form can more accurately reflect the stick–slip characteristics of a disc–block friction system than the model in a fractional form. It can be further applied in the investigation of the dynamic behaviors of high-speed train brake systems.

The dual active bridge (DAB) converter has grown significantly as one of the most important units for energy distribution, connecting various types of renewable energy sources with the DC microgrid. For controlling the DAB converter, moving discretized control set model predictive control (MDCS-MPC) is considered one of the most effective methods because of its advantages, such as high dynamic performance and multiobjective control. However, MDCS-MPC strongly depends on the accuracy of system parameters. Meanwhile, the system parameters can be changed due to temperature drift, manufacturing tolerance, age, and operating circumstances. As a result, the steady-state performance of the output voltage of MDCS-MPC is affected. Motivated by this, this paper proposes MDCS-MPC combined with the parameter identification technique to improve the steady-state performance of the output voltage of the DAB converter. Then, analysis of the percentage of the steady-state error of the output voltage is defined on six model parameters, and sensitivity analysis of two dominant parameters is chosen. After that, a straightforward least-squares analysis (LSA) technique is used to identify the two parameters online. The proposed method is verified through simulation in several different operating scenarios to verify its effectiveness.

An identification strategy based on a machine learning approach is proposed to identify the constitutive parameters of metal sheets. The main novelty lies in the use of Gaussian Process Regression with the objective of identifying the constitutive parameters of metal sheets from the biaxial tensile test results on a cruciform specimen. The metamodel is intended to identify the constitutive parameters of the work hardening law and yield criterion. The metamodel used as input data the forces along both arms of the cruciform specimen and the strains measured for a given set of points. The identification strategy was tested for a wide range of virtual materials, and it was concluded that the strategy is able to identify the constitutive parameter with a relative error below to 1%. Afterwards, an uncertainty analysis is conducted by introducing noise to the force and strain measurements. The optimal strategy is able to identify the constitutive parameters with errors inferior to 6% in the description of the hardening, anisotropy coefficients and yield stresses in the presence of noise. The study emphasizes that the main strength of the proposed strategy relies on the judicious selection of critical areas for strain measurement, thereby increasing the accuracy and reliability of the identification process.

Rigid-reflector spaceborne antennas (RRSAs) are well-suited for high-frequency application scenarios due to their high surface accuracy. However, the low stowing efficiency of RRSAs limits the aperture diameters and further deteriorates the electromagnetic (EM) performances in terms of gain, resolution and sensitivity. After conducting systematic feature analysis with respect to several typical RRSAs, we propose a novel type of RRSA to solve the aforementioned problems. Inspired by the pose adjustment process for a higher stowing efficiency of traditional RRSAs, we also propose a new segmentation scheme of a reflective surface consisting of a deviation-angle panel that facilitates a higher stowing efficiency. Based on this scheme, its corresponding folded configuration is implemented by combining Euler’s rotation theorem and the idea of parameter identification. In addition, we also compare the stowing efficiency of different schemes to verify the high stowing efficiency of the configuration. Finally, we perform mechanism/structure design and deployment dynamics to demonstrate that the antenna can be successfully deployed and exhibits excellent deployment quality. The results suggest that the proposed antenna possesses higher stowing efficiency than that of the same kind, with a stable deployment and interference-free process.

This paper proposes a novel approach to simultaneously identifying multiple critical parameters in a wireless power transfer (WPT) system, such as the resonant frequency, mutual inductance, and load resistance, solely from the primary side. The key is to adopt a primary-side-switch-controlled capacitor (SCC) to ensure that the imaginary part of the input impedance is only caused by the secondary-side reflected impedance at three predesigned frequencies. The DSP controller then samples and processes the primary voltage and current using a gradient descent algorithm to derive the above parameters. After the identification, the SCC adjusts its equivalent capacitance based on the secondary-side practical resonant frequency to ensure a zero-phase angle (ZPA), thereby significantly improving the compatibility of the WPT system with unknown receivers. Compared to the previous frequency-sweeping method, the proposed approach is simpler and more suitable for deployment on the controller. Finally, experimental results demonstrate that the identification error of mutual inductance and resonant frequency are within 7.5% and 2.68%, respectively.

Multiple research studies and seismic data analyses have shown that multi-directional long-period ground motion affects crucial and intricate large-scale structures like oil storage containers, long-span bridges, and high-rise buildings. Seismic damage data show a 3–55% chance of long-period ground motion. To clarify, the chance of occurrence is 3% in hard soil and 83% in soft soil. Due of the above characteristics, the aseismic engineering field requires a realistic stochastic model that accounts for long-period multi-directional ground motion. A weighted average seismic amplification coefficient selected NGA database multi-directional long-period ground motion recordings for this study. Due to the significant low-frequency component in the long-period ground motion, this research uses empirical mode decomposition (EMD) to efficiently decompose it into a composite structure with high- and low-frequency components. Given the above, further investigation is needed on the evolutionary power spectrum density (EPSD) functions of high- and low-frequency components. Analyzing the recorded data will reveal these functions and their corresponding parameters. Proper orthogonal decomposition (POD) is needed to simulate samples of high- and low-frequency components in different directions. These samples can be combined to illustrate multi-directional long-period ground motion. Representative samples exhibit the seismic characteristics of long-period multi-directional ground motion, as shown by numerical examples. This proves the method’s engineering accuracy and usefulness. Moreover, this study used incremental dynamic analysis (IDA) to apply seismic vulnerability theory. This study investigated whether long-period ground motions in both x and multi-directional directions could enhance the seismic response of a high-rise frame structure. By using this method, a comprehensive seismic economic loss rate curve was created, making economic loss assessment clearer. This study shows that multi-directional impacts should be included when studying seismic events and calculating structure economic damages.

Continuous thrust spacecraft in circular orbits have had a great influence on the identification and cataloging of space targets. Gaussian-type orbital element variational equations are simplified and approximated. Ground-based radar observation datasets are transformed into orbit elements datasets. The initial thrust and orbit elements are obtained by optimally solving the spatial parameter error sum of squares minimization problem with the Levenberg–Marquardt method. The simulation analysis is carried out under the high-precision orbit model, and the solution error of tangential acceleration is around 5 × 10−7 m/s2, and that of normal acceleration is around 3 × 10−6 m/s2; the accuracy of the semi-major axis is 350 m, and the accuracy of inclination is 0.095°. The method is applicable to the preliminary identification of thrust and orbit elements for circular orbit continuous thrust spacecraft and can provide reliable initial values for the subsequent precision orbit determination of such spacecraft.

The paper proposes a new method for solving the parameter identification problem for a class of discrete-time linear stochastic systems with multiplicative and additive noises using a numerical gradient-based optimization. The constructed method is based on the application of a covariance UD filter for the above systems and an original method for evaluating state sensitivities within the numerically stable, matrix-orthogonal MWGS transformation. In addition to the numerical stability of the proposed algorithm to machine roundoff errors due to the application of the MWGS-UD orthogonalization procedure at each step, the main advantage of the obtained results is the possibility of analytical calculation of derivatives at a given value of the identified parameter without the need to use finite-difference methods. Numerical experiments demonstrate how the obtained results can be applied to solve the parameter identification problem for the considered stochastic system model.