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The problem of finding the fatigue characteristics of a composite material based on test results is considered. The results of endurance tests of unidirectional polymer composite materials with different initial stiffness, breaking stress and working cycle stress were used as the initial data. As a mathematical model of stiffness degradation, a nonlinear ordinary differential equation with five unknown parameters is used, reflecting characteristic changes in the properties of the material. It is required to find such parameter values that the solution of the differential equation should describe the available test results with sufficient accuracy. The solution procedure is reduced to the problem of optimizing the objective function, the value of which characterizes the achieved accuracy. As optimization methods, a method simulating the behavior of a flock of moths and a method of sequential reduction of the search set were used. A step-by-step algorithm for finding unknown model parameters is proposed, and numerical results of processing input data containing information on changing the elasticity modulus of the composite material in the course of applying load cycles are presented.

Abstract 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.

Objective. The purpose of the study is to solve the problem of calculating the parameters of a linear regression model based on the joint application of anti-robust estimation methods and maximum consistency between the real and calculated values of the dependent variable in continuous form. Method. The unknown parameters of the model are calculated by reducing the original problem to a linear programming problem. Its solution should not cause computational difficulties due to the significant number of developed effective software tools. Result. The generated linear programming problem has a dimension acceptable for real situations. Conclusion. The results of solving a numerical example indicate the effectiveness of the method proposed in the work for calculating the parameters of a linear regression model based on the joint application of anti-robust estimation methods and maximum consistency. The final choice of parameter values remains with the model developer.

Abstract An accurate and efficient approach is required to identify the unknown parameters of solid oxide fuel cell (SOFC) mathematical model for a robust design of any energy system considering SOFC. This research study proposes a modified fire hawk algorithm (MFHA) to determine the values of SOFC model parameters. The performance evaluation of MFHA is tested on two case studies. Firstly, the performance of MFHA is tested on commercially available cylindrical cell developed by Siemens at four temperatures. Results reveal that the least value of sum of squared error (SSE) is 1.04E−05, 2.30E−05, 1.03E−05, and 1.60E−05 at 1073 K, 1173 K, 1213 K, and 1273 K respectively. Results obtained using MFHA have been compared with original fire hawk algorithm (FHA) and other well established and recent algorithms. Secondly, MFHA is implemented for estimating unknown parameters of a 5 kW dynamic tabular stack of 96 cells at various pressures and temperatures. The obtained value of SSE at different temperatures of 873 K, 923 K, 973 K, 1023 K and 1073 K is 1.18E−03, 6.12E−03, 2.21E−02, 5.18E−02, and 6.00E−02, respectively whereas, SSE at different pressures of 1 atm, 2 atm, 3 atm, 4 atm, and 5 atm is 6.05E−02, 6.11E−02, 5.53E−02, 5.11E−02, and 6.64E−02 respectively.

Correctly torquing bone screws is important to achieve good patient outcomes. An automated system has been previously proposed that may allow more objective torquing of bone screws. Here the system is tested vs. a static torque limit in pig bone. 5 screws were first inserted until stripping to calibrate the dynamic and static limits. Then 6 were inserted limited to 80% of the mean stripping torque. Then 7 were inserted using 80% of the dynamic model-predicted stripping torque. The pull-out strength of the dynamic and static insertions were compared and no statistically significant difference was found. Future work should consider at using larger screws and different bone samples, with more repetitions to try and obtain a statistically significant result.

Turntable ladders are flexible large-scale manipulators, so an active oscillation damping is highly valuable. Aiming to support the development and parametrization of the controller, a precise dynamic model of the vehicle is desired. Previous work considering solely the dynamic oscillations of the ladder parts is extended by a dynamic model of the chassis and support structure that includes flexible deformation of the chassis. All contact forces to the ground are calculated because they provide potential for determining dynamic outreach limits in future. As it can happen in practice, the outriggers are able to become fully unloaded in the simulation model. This behaviour is validated by measurements. A sensitivity analysis is used to establish a suitable approach for identification of model parameters. Dynamical measurements illustrate that the combination of two dynamic models of ladder and chassis improves representation of the dynamic oscillations that are crucial for the active oscillation damping control.

Abstract The study introduces a method for identifying the location of receiver coils within a multi‐transmitter IPT system that utilizes shared transmitter coils. In contrast to the conventional receiver coil position recognition methods, this approach eliminates the need for additional detection coils and position sensors. Identifying the location of the receiver coil in a multi‐transmitter IPT system is achieved through the concurrent optimization of the transmitter coil and its electrical characteristics. The study presents the design of coil grouping and control mechanisms for a 3×3 multi‐transmitter. A mathematical model has been developed for the mutual inductance between receiver and transmitter coils on the X–Y plane. Adaptive cooperative particle swarm optimizer (ACPSO) facilitates the identification of mutual inductance and load parameters in a multi‐transmitter IPT system, positioning the receiver coil in a 2D plane using genetic particle swarm optimization (GAPSO) and a mutual inductance mathematical model. The discrepancy in accuracy between the receiver coil's test coordinates and the absolute coordinates remains below 3.5%.

Abstract A dual-arm nursing robot can gently lift patients and transfer them between a bed and a wheelchair. With its lightweight design, high load-bearing capacity, and smooth surface, the coupled-drive joint is particularly well suited for these robots. However, the coupled nature of the joint disrupts the direct linear relationship between the input and output torques, posing challenges for dynamic modeling and practical applications. This study investigated the transmission mechanism of this joint and employed the Lagrangian method to construct a dynamic model of its internal dynamics. Building on this foundation, the Newton-Euler method was used to develop a dynamic model for the entire robotic arm. A continuously differentiable friction model was incorporated to reduce the vibrations caused by speed transitions to zero. An experimental method was designed to compensate for gravity, inertia, and modeling errors to identify the parameters of the friction model. This method establishes a mapping relationship between the friction force and motor current. In addition, a Fourier series-based excitation trajectory was developed to facilitate the identification of the dynamic model parameters of the robotic arm. Trajectory tracking experiments were conducted during the experimental validation phase, demonstrating the high accuracy of the dynamic model and the parameter identification method for the robotic arm. This study presents a dynamic modeling and parameter identification method for coupled-drive joint robotic arms, thereby establishing a foundation for motion control in humanoid nursing robots.

Abstract Parameter identification of solar photovoltaic (PV) cells is crucial for the PV system modeling. However, finding optimal parameters of PV models is an intractable problem due to the highly nonlinear characteristics between currents and voltages in different environments. To address this problem, whale optimization algorithm (WOA)-based meta-heuristic algorithm has turned out to be a feasible and effective approach. As a highly promising optimization algorithm, different enhanced WOA variants have been proposed. Nevertheless, there has been no comparative study of WOA and its variants for parameter identification of PV models so far. To further investigate and analyze the performance of WOA in the studied problem, this work applied and compared WOA and ten enhanced WOA variants for identifying five PV model parameters. Different evaluation indices including solution accuracy, search robustness, and convergence curve were employed to reveal their performance variation. Based on the simulation results, a multi-model statistical analysis with the Friedman test at a confidence level 0.05 was conducted to rank all algorithms. EWOA that hybridizes the sorting-based differential mutation operator and the Lévy flight strategy ranked first and its performance was further verified. Besides, according to the simulation results, possible effective improvement directions for WOA in tackling this intractable problem are concluded to guide future work.

Abstract This paper presents experimental and dynamic modeling research on the rubber bushings of the rear sub-frame. The Particle Swarm Optimization algorithm was utilized to optimize a Backpropagation (BP) neural network, which was separately trained and tested across two frequency ranges: 1–40 Hz and 41–50 Hz, using wideband frequency sweep dynamic stiffness test data. The testing errors at amplitudes of 0.2 mm, 0.3 mm, and 0.5 mm were found to be 1.03%, 3.05%, and 1.96%, respectively. Subsequently, the trained neural network was employed to predict data within the frequency range of 51–70 Hz. To incorporate the predicted data into simulation software, a dynamic model of the rubber bushing was established, encompassing elastic, friction, and viscoelastic elements. Additionally, a novel model, integrating high-order fractional derivatives, was proposed based on the frequency-dependent model for the viscoelastic element. An enhanced Particle Swarm Optimization algorithm was introduced to identify the model's parameters using the predicted data. In comparison to the frequency-dependent model, the new model exhibited lower fitting errors at various amplitudes, with reductions of 3.84%, 3.61%, and 5.49%, respectively. This research establishes a solid foundation for subsequent vehicle dynamic modeling and simulation.

The identification of railway vehicle components’ characteristics from measured data is a challenging task with compelling applications in health monitoring, fault detection, and system prognosis. Usually, though, such systems are highly nonlinear, and naive identification techniques may lead to unstable methods and inaccurate results. In this paper, we show that these issues can be easily tackled with the recently introduced proximal Gauss–Newton method, which we employ to identify the parameters of a railway nonlinear suspension system. In the proposed model, the parameters are subject to safety bounds in form of box constraints, which allows preventing nonphysical solutions. The suspension system we consider is highly nonlinear due to the presence of an airspring in the secondary suspension, which we introduce in a simplified Berg model. Numerical examples, featuring data corrupted by various noise levels, demonstrate the accuracy and efficiency of our proposed method. Comparisons with state-of-the-art approaches are also provided.

This paper sets pioneering research which investigates the parametric identification of thermoelectric modules (TEMs) through the employment of enhanced slime mould algorithm (ESMA). The proposed method incorporates a pair of modifications to the standard slime mould algorithm (SMA). Primary modification encloses computation of random average position between the slimes' current individual position and best individual position towards resolution of local optima issue. Subsequent modification then involves substitution of an exponential function to the existing tangent hyperbolic function within formula p of the standard SMA in enabling improved probability variants via the selection of updated equations. Competency of the proposed algorithm in generating the optimal parameters for TEMs was appraised based on 21 benchmarked design parameters, following the objective of root mean square error (RMSE) minimization between the temperature of both actual and estimated models. Acquired results which demonstrate lower values of RMSE and parameter deviation index against the standard SMA and other preceding algorithms such as particle swarm optimization, sine cosine algorithm, moth flame optimizer and ant lion optimizer ultimately verified ESMA’s efficacy as an effective approach for accurate model identification.

The determination of unknown parameters of photovoltaic models has always been a difficult problem in the field of nonlinear optimization, which is inseparable from the energy conversion efficiency of photovoltaic power generation systems. Due to the high degree of nonlinearity and difficulty in parameter identification of photovoltaic models with multiple diode branches, this paper proposes a novel and efficient evolutionary algorithm based on success-history adaptation differential evolution with linear population size reduction (L-SHADE)—Dynamic L-SHADE with parameter decomposition method (DaL-SHADE) to identify unknown parameters of photovoltaic models. In DaL-SHADE, a dynamic crossover rate sorting technology is introduced, which establishes the relationship between the population and its crossover rate that is ignored in L-SHADE, and thereby the optimization ability of the algorithm to identify unknown parameters is improved. Secondly, the unknown parameters of different types of photovoltaic models are decomposed into linear parameters and nonlinear parameters, so that the dimensionality of the photovoltaic model is reduced. Therefore, DaL-SHADE not only has efficient optimization capabilities but also reduces the dimensionality of different types of photovoltaic models. Through the examination of four photovoltaic models with different degrees of nonlinearization, root mean square error obtained by DaL-SHADE is better than that of the comparison algorithms.

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.

To evaluate the shear-resistant behavior of wooden dowels used in Blockhaus shear walls under cyclic load, 19 specimens under ten groups of conditions were prepared and tested. The failure modes, hysteresis curves, mechanical properties, stiffness degradations, and energy dissipation capacities of the specimens were studied. The test results showed that with the increase in the number of dowels, the initial stiffness and peak load of the specimens increased greatly. The diameter of the dowels had little influence on the mechanical properties of the specimens. Furthermore, the test findings demonstrated that the pretension load between the walls greatly enhanced the initial stiffness and energy dissipation capacity of specimens. A simplified finite element model was established in Opensees. Considering the effect of material variability, the parameters of single dowel shear spring and friction spring were identified by Genetic Algorithm with modified objective function in Matlab. The identified parameters were applied to the finite element model of the multi-dowel specimens. The simulation results were in good agreement with the test results, and the validity of the numerical model and parameter identification method was verified.

When a certain type of aircraft is maneuvered laterally, the aileron and the spoiler will be coupled and deflected to realize the rolling motion of the aircraft. The existing aerodynamic parameter identification methods cannot separately identify the control derivatives of the aileron and the spoiler. Based on the design logic of the aircraft control law and the loading characteristics of the structure during the roll motion, a new method for identifying the aileron control derivative based on the flight measured load is proposed. Firstly, the measurement method of wing aerodynamic load in maneuvering flight is proposed by measuring the structural loads of each load-testing section of the wing in maneuvering flight and combining the structural mass and acceleration distribution outside the section. Secondly, combining the instantaneous rolling moment balance of aircraft lateral control based on the equation and control surface deflection logic, the identification process of aileron control derivatives during roll recovery is deduced, and the identification method of aileron control derivatives based on the measured load of the wing is proposed. Finally, based on the aforementioned method, the identification of aileron control derivatives is carried out, and the influence of different Mach numbers on the derivative of aileron control is analyzed. The results show that the aileron control derivative can be identified based on the flight measured load, and the concentration of the aileron control derivative results is better at the same Mach number. As the Mach number increases, the aileron control effectiveness will decrease. The measured results of flight loads have the potential to be applied to the identification of aerodynamic parameters.

To evaluate the shear-resistant behavior of wooden dowels used in Blockhaus shear walls under cyclic load, 19 specimens under ten groups of conditions were prepared and tested. The failure modes, hysteresis curves, mechanical properties, stiffness degradations, and energy dissipation capacities of the specimens were studied. The test results showed that with the increase in the number of dowels, the initial stiffness and peak load of the specimens increased greatly. The diameter of the dowels had little influence on the mechanical properties of the specimens. Furthermore, the test findings demonstrated that the pretension load between the walls greatly enhanced the initial stiffness and energy dissipation capacity of specimens. A simplified finite element model was established in Opensees. Considering the effect of material variability, the parameters of single dowel shear spring and friction spring were identified by Genetic Algorithm with modified objective function in Matlab. The identified parameters were applied to the finite element model of the multi-dowel specimens. The simulation results were in good agreement with the test results, and the validity of the numerical model and parameter identification method was verified.

Abstract Accurate modeling and parameter identification of photovoltaic (PV) cells is a difficult task due to the nonlinear characteristics of PV cells. The goal of this paper is to propose a multi strategy sine–cosine algorithm (SCA), named enhanced sine–cosine algorithm (ESCA), to evaluate nondirectly measurable parameters of PV cells. The ESCA introduces the concept of population average position to increase the population exploration ability, and at the same time introduces the personal destination agent mutation mechanism and competitive selection mechanism into SCA to provide more search directions for ESCA while ensuring the search accuracy and diversity maintenance. To prove that the proposed ESCA is the best choice for extracting nondirectly measurable parameters of PV cells, ESCA is evaluated by the single‐diode model, the double‐diode model, the three‐diode model, and the photovoltaic module model (PVM), and compared with eight existing popular methods. Experimental results show that ESCA outperforms similar methods in terms of diversity maintenance, high efficiency, and stability. In particular, the proposed ESCA method is less than the SCA by 0.081, 0.144, and 0.578 in the standard deviation statistics metrics of the three PVM models (PV‐PWP201, STM6‐40/36, and STP6‐120/36), respectively. Therefore, the proposed ESCA is an accurate and reliable method for parameter identification of PV cells.

Abstract Exoskeletons generally require accurate dynamic models to design the model-based controller conveniently under the human-robot interaction condition. However, due to unknown model parameters such as the mass, moment of inertia and mechanical size, the dynamic model of exoskeletons is difficult to construct. Hence, an enhanced whale optimization algorithm (EWOA) is proposed to identify the exoskeleton model parameters. Meanwhile, the periodic excitation trajectories are designed by finite Fourier series to input the desired position demand of exoskeletons with mechanical physical constraints. Then a backstepping controller based on the identified model is adopted to improve the human-robot wearable comfortable performance under cooperative motion. Finally, the proposed Model parameters identification and control are verified by a two-DOF exoskeletons platform. The knee joint motion achieves a steady-state response after 0.5 s. Meanwhile, the position error of hip joint response is less than 0.03 rad after 0.9 s. In addition, the steady-state human-robot interaction torque of the two joints is constrained within 15 $${\text{N}\cdot\text{m}}$$ N · m . This research proposes a whale optimization algorithm to optimize the excitation trajectory and identify model parameters. Furthermore, an enhanced mutation strategy is adopted to avoid whale evolution's unsatisfactory local optimal value.

This study focuses on the monitoring, transmission, recognition and detection of snoring signals and their relationship with obstructive sleep apnea. To achieve this purpose, the ESP32 microcontroller and a MEMS technology microphone were used to capture and measure characteristic parameters of snoring signals, such as their intensity, frequency and duration. In addition, the WiFi radio interface was used to send the signals to a server where the information was processed, the snoring was detected, linked to a chatbot in Nodred to show the user in a graphical interface his diagnosis of the snoring level. This comprehensive approach allows real-time, wireless monitoring of snoring, leading to a less invasive diagnosis of obstructive sleep apnea.

The temperature control of the air-cooled proton exchange membrane fuel cell (PEMFC) is important for effective and safe operation. To develop a practical and precise controller, this study combines the Radial Basis Function (RBF) neural network with Back Propagation neural network adaptive Proportion Integration Differentiation (BP-PID), and then a metaheuristic algorithm is used to optimize the parameters of RBF-BP-PID for further improvement in temperature control. First, an air-cooled PEMFC system model is established. To match the simulation data with the experimental data, Teaching Learning Based Optimization–Differential Evolution (TLBO-DE) is proposed to identify the unknown parameters, and the maximum relative error is

Modeling methods of a robot dynamics include (i) obtaining the equations of motion and identifying the dynamics parameters, and (ii) system identification of the robot dynamical characteristics. However, the parameters obtained in (i) are approximate solutions whose accuracy depends on the motion and environment in which the robot is operated, because the actual robot is finer and includes un-modeled dynamics. Therefore, an appropriate evaluation index is required for identification. In (ii), the accuracy is low in the high-frequency domain due to the S/N ratio problem, which often causes spillover. In addition, in closed-loop identification, the inverse function of the controller is identified due to noises. In this paper, we focus on the periodic motion of the end-effector of a planar 3-link manipulator and derive an error equation that exploits the periodicity of the motion to simultaneously identify the dynamics parameters and spillover. In particular, by selecting the identification object to realize an open-loop identification, the identification of spillover has high accuracy. Based on the obtained dynamic model, a control system is designed to suppress the error, and experiments show to verify that the proposed method achieves sub-millimeter order accuracy control.

ObjectiveThe success rate of achieving seizure freedom after radiofrequency thermocoagulation surgery for patients with refractory focal epilepsy is about 20–40%. This study aims to enhance the prediction of surgical outcomes based on preoperative decisions through network model simulation, providing a reference for clinicians to validate and optimize surgical plans.MethodsTwelve patients with epilepsy who underwent radiofrequency thermocoagulation were retrospectively reviewed in this study. A coupled model based on model subsets of the neural mass model was constructed by calculating partial directed coherence as the coupling matrix from stereoelectroencephalography (SEEG) signals. Multi-channel time-varying model parameters of excitation and inhibitions were identified by fitting the real SEEG signals with the coupled model. Further incorporating these model parameters, the coupled model virtually removed contacts destroyed in radiofrequency thermocoagulation or selected randomly. Subsequently, the coupled model after virtual surgery was simulated.ResultsThe identified excitatory and inhibitory parameters showed significant difference before and after seizure onset (p

The role of load modeling in power systems is crucial for both operational and regulatory considerations. It is essential to develop an effective and reliable method for optimizing load modeling parameter identification. In this paper, the dung beetle algorithm is improved by using the good point set, and a load model parameter identification strategy based on the good point set dung beetle optimization algorithm (GDBO) within the framework of the measurement-based load modeling method. The proposed parameter identification strategy involves utilizing PMU voltage data as input, selecting a comprehensive load model, and refining the initialization process based on the good point set to mitigate the influence of local maxima. Through iterative optimization of the objective function using the Dung Beetle Optimizer (DBO) algorithm, the optimal parameters for the comprehensive load model are determined, enhancing the model’s ability to accurately capture the power curve. Analysis of examples pertaining to PMU-measured modeling parameter identification reveals that the proposed GDBO algorithm, which incorporates a good point set, outperforms alternative methods such as the improved differential evolution algorithm (IDE), particle swarm optimization algorithm (PSO), grey wolf optimization algorithm (GWO), and conventional DBO algorithm. This demonstrates the superior performance of the introduced approach in the context of load model parameter identification.

Aiming at the current lithium-ion battery storage power station model, which cannot effectively reflect the battery characteristics, a proposed electro-thermal coupling modeling method for storage power stations considers the characteristics of the battery body by combining the equivalent circuit model and accounting for the effect of temperature on the battery. Based on the modeling of a single lithium-ion battery, the equivalent circuit model and thermal model are integrated to create the battery’s electro-thermal coupling model. The parameters of this coupling model are determined using the particle swarm algorithm. On this basis, the battery compartment model of the energy storage station is analyzed and verified by utilizing the circuit series–parallel connection characteristics. Subsequently, the electro-thermal coupling model of the energy storage station is established. The dual Kalman filter algorithm is utilized to simulate and validate the electric–thermal coupling model of the energy storage power station, considering ontological factors such as battery voltage, current, and temperature. The results demonstrate that the established coupling model can accurately determine the SOC and temperature of the power station. This ability allows for a more precise reflection of the battery characteristics of the energy storage station. It also validates the accuracy and effectiveness of the electric–thermal coupling model of the energy storage station. This finding is crucial for assessing the state and ensuring the safe operation of the battery system in the energy storage station.

Accuracy of a lithium-ion battery model is pivotal in faithfully representing actual state of battery, thereby influencing safety of entire electric vehicles. Precise estimation of battery model parameters using key measured signals is essential. However, measured signals inevitably carry random noise due to complex real-world operating environments and sensor errors, potentially diminishing model estimation accuracy. Addressing the challenge of accuracy reduction caused by noise, this paper introduces a Bias-Compensated Forgetting Factor Recursive Least Squares (BCFFRLS) method. Initially, a variational error model is crafted to estimate the average weighted variance of random noise. Subsequently, an augmentation matrix is devised to calculate the bias term using augmented and extended parameter vectors, compensating for bias in the parameter estimates. To assess the proposed method's effectiveness in improving parameter identification accuracy, lithium-ion battery experiments were conducted in three test conditions—Urban Dynamometer Driving Schedule (UDDS), Dynamic Stress Test (DST), and Hybrid Pulse Power Characterization (HPPC). The proposed method, alongside two contrasting methods—the offline identification method and Forgetting Factor Recursive Least Squares (FFRLS)—was employed for battery model parameter identification. Comparative analysis reveals substantial improvements, with the mean absolute error reduced by 25%, 28%, and 15%, and the root mean square error reduced by 25.1%, 42.7%, and 15.9% in UDDS, HPPC, and DST operating conditions, respectively, when compared to the FFRLS method.

Real-world parameter identification problems require determining the bounds that cover the unknown solutions. This paper presents an adaptive differential evolution algorithm with a bound adjustment strategy (ADEBAS) for solving nonlinear parameter identification problems. The adjustment strategy detects the parameter-bound violations of mutant vectors during the evolution process and gradually extends the bounds. The algorithm adaptively uses two mutation strategies and two ranges of crossover rate to balance the population diversity and convergence speed. Experimental results show that ADEBAS can solve 24 nonlinear regression tasks from the National Institute of Standards and Technology benchmark with accurate estimation and reliability. It also outperforms the compared methods on real-world parameter identification problems.

A model-based controller is effective for highly accurate and fast control of a robot. The dynamics model is derived from its equations of motion, and the minimum set of the dynamics parameters are experimentally identified. However, the experimental data includes the influence of both noise and un-modeled dynamics, the obtained model will be one of approximated solutions. From these considerations, the approximated model has to well represent the robot dynamics around the reference motion, and for high accuracy, new data needs to be added every time an experiment is conducted, which causes a lot of computation. The authors have proposed stochastic identification method to obtain suitable parameters for control system design. However, in this method, because of computational complexity of weighted least square mean, it is difficult to add new motion data. In this paper, we propose a sequentially updating parameter identification method based on statistical properties of the conventional method. By synthesizing the covariance of the parameter error, the nominal parameters are updated. The proposed method is performed to experiments using a planar 3-link manipulator, and its effectiveness is evaluated.

High-speed automatic train operation (ATO) systems inherently exhibit strong nonlinearity and uncertainty. In view of the characteristics of nonlinearity and time-variability of high-speed train model parameters, this paper proposes a feed forward adaptive-generalized model predictive control (FA-GPC) method for dynamic optimization control of the ATO system along with a constrained multi-object predictive controller. Based on a multi-particle train model, an initial analysis explores the impacts of additional resistance changes on train operation. Subsequently, a multi-object performance indicator function containing control input constraints is constructed, combined with key indicators during the operation of high-speed trains, such as speed tracking accuracy, stopping accuracy, and riding comfort. Furthermore, a feed forward generalized prediction speed tracking control algorithm is designed based on the multi-object function, aiming to solve controller overshoot due to additional resistance changes and enhance control convergence rates. Taking into account various factors including influences of external environments and passenger movement during train operation, the resistance changes greatly, making it difficult to establish an accurate mathematical model, a constrained variable forgetting factor-recursive least square method is incorporated to identify the controlled auto-regressive integrated moving average model (CARIMA) of the train control system under different operational conditions. This approach aims to improve the robustness of the control system. Simulation results show that, compared with traditional GPC in the absence of the feed forward functionality and PID controller, the proposed feed forward generalized controller demonstrates good cruise control speed tracking accuracy within a ±0.5 km/h range under different line conditions and strong robustness thanks to the adaptive modification to the feed forward generalized predictive control algorithm for better performance in strong disturbance conditions.

ObjectivesAiming at the challenge of accurate diving modeling of a smart float, an anti-saturation and noise least squares (ASNLS) algorithm is proposed in this paper to achieve diving multi-parameter identification and depth prediction. MethodsFirstly, the nonlinear motion characteristics of the smart float actuator were included in the gray box-based diving model to better fit the actual model, and the continuous diving motion equation was transformed into a discrete form to match the real-world discrete data sampling. Subsequently, the aforementioned discrete diving model was constructed into a correlation form to attenuate the influence of data noise. Finally, by adjusting the values of the covariance matrix, the designed diving parameter identification algorithm achieved resistance to data saturation. ResultsBased on the data of the South China Sea deep diving experiment of the smart float in 2021, diving model parameter identification and depth prediction are carried out. The results demonstrate that the ASNLS algorithm has faster convergence speed (31.8% higher than the least squares algorithm) and smaller depth prediction error (average absolute percentage errors less than 9% at different depths) than both the traditional least squares algorithm and supports the vector machine algorithm.ConclusionsConsequently, the ASNLS algorithm can provide effective support for the depth control and prediction of the smart float.

A steady-state sinusoidal and distortion-free excitation source is very important for the accuracy and consistency of the calibration parameters of micro-electro-mechanical systems (MEMS) inertial sensors. To solve the problem that the current MEMS inertial measurement unit (IMU) calibration device is unable to reproduce the spatial motion of linear and angular vibration coupling, research topics on the coupling vibration characteristics and parameter identification for an electromagnetic linear-angular vibration exciter are proposed. This research paper used Ampere’s law and Lorentz force to establish the analytical expressions for the electromagnetic force and electromagnetic torque of the electromagnetic linear-angular vibration exciter. Then, the main purpose of this paper is to establish uniaxial and coupled vibration electromechanical analogy models containing mechanical parameters based on the admittance-type electromechanical analogy principle, and the parameter identification model is also obtained by combining the impedance formula with the additional mass method. Finally, the validity of the coupling vibration characteristics and the parameter identification model are verified by the frequency response simulation and the additional mass method, and the relative error of each parameter identification is within 5% in this paper.

Objective. The aim of the study is to develop an algorithm for identifying the parameters of a homogeneous nested piecewise linear regression model of the first type by the method of least modules. Method. Estimation of its unknown parameters is carried out with the help of reduction to the problem of linear Boolean programming. Its solution should not cause computational difficulties due to a significant number of effective software tools - for example, the well-established and freely available program LPsolve on the Internet. Result. The generated linear programming problem has an acceptable dimension for solving practical modeling problems. Conclusion. The results of solving a numerical example indicate the effectiveness of the method proposed in the work for calculating parameter estimates for a homogeneous nested piecewise linear regression model of the first type by the method of least modules.

This paper proposes a novel algorithm for the parameter identification of induction motors based on third-order generalized integrators (TOGIs), where both the electrical and mechanical parameters are estimated. First, AC voltages of 0.7 V at 200 Hz and 1.2 V at 5 Hz superimposed on a DC bias component are injected into the induction motor by a PWM inverter, from which the amplitude of the stator current and the phase angle between stator voltage and stator current are calculated by the TOGI. These signals are used to estimate the stator transient inductance, rotor resistance, and rotor time constant precisely. On the other hand, a dynamic test is performed to identify the mechanical parameter. A sinusoidal speed reference is commanded to the induction motor, and the corresponding motor torque signal is then utilized to estimate the moment of inertia with the help of the TOGI. Since the TOGI behaves as an adaptive filter, the adverse effects of DC-offset and measurement noises are mitigated. The effectiveness of the proposed identification method has been verified by experimental results, showing errors of +2.6% for the stator transient inductance, +2.8% for the rotor resistance, and -1.8% for the rotor time constant compared to those obtained following IEEE Std. 112; and 1.3% for the moment of inertia compared to the value in the technical datasheet.

This paper proposes an approach to identifying the core aspect ratios of three-limb core-type power transformers. Analyzing transformer transients, such as inrush currents, is essential for ensuring uninterrupted power delivery, particularly in grids dominated by Inverter-Based Resources (IBRs). Topological-based transformer models, such as the Unified Magnetic Equivalent Circuit (UMEC) used in commercial simulators (e.g., PSCAD and RSCAD/RTDS), accurately represent transformers. However, these models require data on transformer core dimensions, which are often unavailable or proprietary. Consequently, despite the accuracy of topological-based models, they are less applicable in grid analysis. The proposed approach utilizes a Machine Learning (ML) algorithm, namely Extreme Gradient Boosting (XGBoost), to identify the transformer core aspect ratios using only current and voltage measurements when the transformer is in the steady state during no-load conditions. The performance of the proposed method is evaluated using MATLAB/Simscape for modeling transformers and MATLAB for running the XGBoost algorithm. It is inferred from the test results that the proposed method is able to identify the core aspect ratios of three-limb core-type transformers with satisfactory precision. The proposed method will enhance the accuracy of transient analysis, and it could be extended to cover four-limb and five-limb core-type transformers in the future.

This article covers the design, implementation, mathematical modelling, and control of a multivariable, underactuated, low-cost, three-degrees-of-freedom experimental helicopter system (namely a 3-DOF helicopter). The system is considered an essential benchmark for the development and evaluation of control algorithms and identification methods as it mimics real helicopters and UAVs. The system is multivariable and difficult to control due to its underactuation characteristics. In addition, it presents further challenges in modelling and control analysis in terms of high nonlinearity, coupling, uncertainty, and unmodeled dynamics. In this scene, the system is designed, developed, and operated virtually in SolidWorks using the necessary hardware and software. A new accurate multivariable coupled nonlinear mathematical model is derived and proposed. The model takes into account the complex dynamics and interactions between the system components, providing a more comprehensive representation of the real system compared to previous models. System parameters are then identified using the Sum of the Square Error (SSE) algorithm, and the mathematical model is compared to the practical results in many different scenarios via Simulink. Experimental tests confirmed the mathematical model and robust performance of the implemented experimental 3-DOF helicopter system, as well as the ability of the proposed controller to stabilize the helicopter and track different trajectories in the presence of uncertainties and cross-coupling effects.

Joint hyper-resistance is a common symptom in neurological disorders. It has both neural and non-neural origins, but it has been challenging to distinguish different origins based on clinical tests alone. Combining instrumented tests with parameter identification based on a neuromechanical model may allow us to dissociate the different origins of joint hyper-resistance in individual patients. However, this requires that the model captures the underlying mechanisms. Here, we propose a neuromechanical model that, in contrast to previously proposed models, accounts for muscle short-range stiffness (SRS) and its interaction with muscle tone and reflex activity. We collected knee angle trajectories during the pendulum test in 15 children with cerebral palsy (CP) and 5 typically developing children. We did the test in two conditions – hold and pre-movement – that have been shown to alter knee movement. We modeled the lower leg as an inverted pendulum actuated by two antagonistic Hill-type muscles extended with SRS. Reflex activity was modeled as delayed, linear feedback from muscle force. We estimated neural and non-neural parameters by optimizing the fit between simulated and measured knee angle trajectories during the hold condition. The model could fit a wide range of knee angle trajectories in the hold condition. The model with personalized parameters predicted the effect of pre-movement demonstrating that the model captured the underlying mechanism and subject-specific deficits. Our model may help with the identification of neural and non-neural origins of joint hyper-resistance and thereby opens perspectives for improved diagnosis and treatment selection in children with spastic CP, but such applications require further studies to establish the method’s reliability.

Precise modeling of power systems is vital to ensure stability, reliability, and secure operations. In power industrial settings, model parameters can become skewed over time due to prolonged device usage or modifications made to the control systems. Doubly-Fed Induction Generator (DFIG), one of the most prevalent generators in wind farms, is sensitive to transient occurrences. Consequently, parameter calibration of DFIG becomes a crucial focal point in power system planning and operational studies. In this paper, two baseline approaches are first developed to identify the potentially harmful parameters of the DFIG system, including the Particle Swarm Optimization (PSO) method and the state-of-the-art off-policy Reinforcement Learning (RL) method, Soft Actor-Critic (SAC). The outcomes demonstrated that the SAC method outperformed PSO, resulting in an impressive reduction of 74.67% Mean Squared Error (MSE) and a more efficient testing period. In further exploration, a novel hybrid approach called SAC-PSO is developed, with SAC being the teacher of PSO to tackle scenarios with multiple potential solutions. The results exhibited an even greater enhancement over using SAC alone, leading to a remarkable reduction of 87.84% MSE during the testing phase. The proposed method can also effectively apply to a power plant incorporating multiple wind generators.

Computational biomechanical analysis plays a pivotal role in understanding and improving human movements and physical functions. Although physics-based modeling methods can interpret the dynamic interaction between the neural drive to muscle dynamics and joint kinematics, they suffer from high computational latency. In recent years, data-driven methods have emerged as a promising alternative due to their fast execution speed, but label information is still required during training, which is not easy to acquire in practice. To tackle these issues, this paper presents a novel physics-informed deep learning method to predict muscle forces without any label information during model training. In addition, the proposed method could also identify personalized muscle-tendon parameters. To achieve this, the Hill muscle model-based forward dynamics is embedded into the deep neural network as the additional loss to further regulate the behavior of the deep neural network. Experimental validations on the wrist joint from six healthy subjects are performed, and a fully connected neural network (FNN) is selected to implement the proposed method. The predicted results of muscle forces show comparable or even lower root mean square error (RMSE) and higher coefficient of determination compared with baseline methods, which have to use the labeled surface electromyography (sEMG) signals, and it can also identify muscle-tendon parameters accurately, demonstrating the effectiveness of the proposed physics-informed deep learning method.

This work proposes a cell-to-cell voltage balance and flexible power sharing control strategy for 1-phase utility interfacing 1-phase modular high frequency AC link (HFAC-L) converter. Input series-output parallel (ISOP) configuration having cascaded multilevel front end converter (CMFEC) in the first stage and dual active bridge (DAB) converter in the second stage is the topology of focus in this work. This topology is extensively used in the solid state transformer (SST). Flexible power sharing is accomplished by estimating the high frequency link (HF-link) fundamental current component of each cell. No high bandwidth current sensors are used. Moreover, this scheme also incorporates parametric estimation of each HF-link inductor. This feature makes it robust to gradual ageing related parametric variation. Irrespective of the number of cells, this strategy requires only two low bandwidth current sensors, for grid current and load current measurements. Compared to erstwhile approaches, this flexible power sharing strategy allows for controlled zero power (phase-shedding/plug-out) operation of a cell while still regulating its DC link voltage. The proposed strategy also makes the subsequent phase-addition/plug-in process seamless without the need for pre-charge circuits. This scheme can also be reconfigured to operate in output current regulation mode. These features are validated through experiments performed on a 1.6 kW laboratory prototype.

This paper addresses the problem of underestimated temperature measurements in practical proton exchange membrane (PEM) electrolyzer engineering due to heat losses by enhancing the existing second-order RC equivalent circuit model of PEM electrolyzers. We present a novel engineering circuit model for PEM electrolyzers, incorporating the effects of heat losses from gases and pipelines. The objective is to enhance the model’s ability to predict electrolyzer performance and align control strategies with the realities of engineering practice. However, the PEM electrolyzer model is complex, being time-varying and nonlinear due to multi-physics field coupling. The parameters of the equivalent circuit are changed by the electrical energy input and its own state. To tackle the problem of parameter variation, firstly, a recursive identification algorithm is employed to estimate the internal equivalent circuit parameters of the engineering model. Then, the additional resistance is fitted according to the relationship between heat loss and current to complete the engineering circuit model identification. Finally, using MATLAB to construct an engineering model and validate the effectiveness of the proposed identification algorithm.

This paper investigates the adaptive trajectory tracking control problem and the unknown parameter identification problem of a class of rotor-missiles with parametric system uncertainties. First, considering the uncertainty of structural and aerodynamic parameters, the six-degree-of-freedom (6DoF) nonlinear equations describing the position and attitude dynamics of the rotor-missile are established, respectively, in the inertial and body-fixed reference frames. Next, a hierarchical adaptive trajectory tracking controller that can guarantee closed-loop stability is proposed according to the cascade characteristics of the 6DoF dynamics. Then, a memory-augmented update rule of unknown parameters is proposed by integrating all historical data of the regression matrix. As long as the finitely excited condition is satisfied, the precise identification of unknown parameters can be achieved. Finally, the validity of the proposed trajectory tracking controller and the parameter identification method is proved through Lyapunov stability theory and numerical simulations.

The subsynchronous oscillations (SSOs) related to renewable generation seriously affect the stability and safety of the power systems. To realize the dynamic monitoring of SSOs by utilizing the high computational efficiency and noise-resilient features of the matrix pencil method (MPM), this paper proposes an improved MPM-based parameter identification with synchrophasors. The MPM is enhanced by the angular frequency fitting equations based on the characteristic polynomial coefficients of the matrix pencil to ensure the accuracy of the identified parameters, since the existing eigenvalue solution of the MPM ignores the angular frequency conjugation constraints of the two fundamental modes and two oscillation modes. Then, the identification and recovery of bad data are proposed by utilizing the difference in temporal continuity of the synchrophasors before and after noise reduction. The proposed parameter identification is verified with synthetic, simulated, and actual measured phase measurement unit (PMU) data. Compared with the existing MPM, the improved MPM achieves better accuracy for parameter identification of each component in SSOs, better real-time performance, and significantly reduces the effect of bad data.

Abstract On-site and real-time non-destructive measurement of elastic constants for materials of a component in a in-service structure is a challenge due to structural complexities, such as ambiguous boundary, variable thickness, nonuniform material properties. This work develops for the first time a method that uses ultrasound echo groups and artificial neural network (ANN) for reliable on-site real-time identification of material parameters. The use of echo groups allows the use of lower frequencies, and hence more accommodative to structural complexity. To train the ANNs, a numerical model is established that is capable of computing the waveform of ultrasonic echo groups for any given set of material properties of a given structure. The waveform of an ultrasonic echo groups at an interest location on the surface the structure with material parameters varying in a predefined range are then computed using the numerical model. This results in a set of dataset for training the ANN model. Once the ANN is trained, the material parameters can be identified simultaneously using the actual measured echo waveform as input to the ANN. Intensive tests have been conducted both numerically and experimentally to evaluate the effectiveness and accuracy of the currently proposed method. The results show that the maximum identification error of numerical example is less than 2%, and the maximum identification error of experimental test is less than 7%. Compared with currently prevailing methods and equipment, the proposefy the density and thickness, in addition to the elastic constants. Moreover, the reliability and accuracy of inverse prediction is significantly improved. Thus, it has broad applications and enables real-time field measurements, which has not been fulfilled by any other available methods or equipment.

In this paper, a creative dung beetle optimization (CDBO) algorithm is proposed and applied to the offline parameter identification of permanent magnet synchronous motors. First, in order to uniformly initialize the population state and increase the population diversity, a strategy to improve the initialization of the dung beetle population using Singer chaotic mapping is proposed to improve the global search performance; second, in order to improve the local search performance and enhance the convergence accuracy of the algorithm, a new dung beetle position update strategy is designed to increase the spatial search range of the algorithm. Simulation results show that the proposed optimization algorithm can quickly and accurately identify parameters such as resistance, inductance, and magnetic chain of the PMSM, with significant improvements in convergence algebra, identification accuracy and stability.

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.