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In this paper, an artificial neural network (ANN) is used for isolating faults and degradation phenomena occurring in photovoltaic (PV) panels. In the literature, it is well known that the values of the single diode model (SDM) associated to the PV source are strictly related to degradation phenomena and their variation is an indicator of panel degradation. On the other hand, the values of parameters that allow to identify the degraded conditions are not known a priori because they can be different from panel to panel and are strongly dependent on environmental conditions, PV technology and the manufacturing process. For these reasons, to correctly detect the presence of degradation, the effect of environmental conditions and fabrication processes must be properly filtered out. The approach proposed in this paper exploits the intrinsic capability of ANN to map in its architecture two effects: (1) the non-linear relations existing among the SDM parameters and the environmental conditions, and (2) the effect of the degradation phenomena on the I-V curves and, consequently, on the SDM parameters. The ANN architecture is composed of two stages that are trained separately: one for predicting the SDM parameters under the hypothesis of healthy operation and the other one for degraded condition. The variation of each parameter, calculated as the difference of the output of the two ANN stages, will give a direct identification of the type of degradation that is occurring on the PV panel. The method was initially tested by using the experimental I-V curves provided by the NREL database, where the degradation was introduced artificially, later tested by using some degraded experimental I-V curves.

The paper presents application of a modified, symmetrical Bouc-Wen model to simulate the mechanical behaviour of high-frequency piezoelectric actuators (PAs). In order to identify parameters of the model, a two-step algorithm was developed. In its first stage, the mechanical parameters were identified by taking into account their bilinear variability and using a square input voltage waveform. In the second step, the hysteresis parameters were determined based on a periodic excitation. Additionally, in order to reduce the influence of measurement errors in determination of selected derivatives the continuum wavelet transform (CWT) and translation-rotation transformation (TRT) methods were applied. The results proved that the modified symmetrical Bouc-Wen model is able to describe the mechanical behaviour of PAs across a wide frequency range.

The optimal charge power and position necessary for forming the maximum possible size of the crater along with preservation of the integrity of the two nearby objects with the numerical quasiconformal mapping methods with the alternate parameterization of the of the medium and process character are established. Unambiguously the boundaries of crater, pressed and disturbed soil zones are identified and the corresponding field dynamic grid is built. A number of experiments was held on the basis of the developed algorithm and their results were analyzed.

Accurate estimation of the state of charge (SOC) of a lithium-ion battery is one of the most crucial issues of battery management system (BMS). Existing methods can achieve accurate estimation of the SOC under stable working conditions. However, they may result in inaccuracy under unstable working conditions such as dynamic cycles and different temperature conditions. This is due to the fact that the dynamic behaviors of battery states have not been considered by the parameter identification methods. In this paper, a SOC and parameter joint estimation method is put forward, where the battery model parameters are identified in real time by a particle filter (PF) with consideration of the battery states. Meanwhile, a cubature Kalman filter (CKF) is used to estimate SOC. Then, experiments under dynamic cycles and different temperature conditions are undertaken to assess the performance of the proposed algorithm when compared with the existing joint estimations. The results show that the proposed joint method can achieve a high accuracy and robustness for SOC estimation. [ABSTRACT FROM AUTHOR]

Aiming at evaluating the state of the photovoltaic (PV) array and improving the reliability of the PV system, a fault diagnosis method for PV arrays based on fault parameters identification is proposed in this paper. Compared with existing fault diagnosis methods, the proposed method has advantages of recognizing concurrent faults and describing each fault quantitatively by identifying fault parameters from the measured current-voltage (I–V) curve of the PV array. The methodology consists of three parts. Firstly, functional relationships between unknown parameters in the one-diode model of PV cells with environmental parameters are obtained by parameters extraction. Secondly, a code-based fast fault simulation model (CFFSM) is established to simulate I–V curves of the PV array under various faulted conditions. Thirdly, by determining the fault parameters to be identified and constructing an objective function that is the error between the simulated I–V curve with the measured I–V curve, an optimization problem is formulated, in which optimal fault parameters are identified by applying the differential evolution (DE) algorithm. The validation experiments in summer and early spring show that the proposed diagnosis method can identify the parameters of up to three concurrent faults, including partial shading, short circuit, and increased series-resistance losses, under good irradiance condition with high accuracy. • The proposed diagnosis method can identify concurrent faults in PV arrays. • The proposed diagnosis method can give quantitative description to each fault. • The proposed simulation model is code based and can simulate three kinds of faults. • The proposed simulation model for PV arrays has good computational efficiency. [ABSTRACT FROM AUTHOR]

In this paper, a key differential equation is proposed to formulate fatigue damage evolution in metallic alloys under multiaxial, multiblock, proportional loadings in high cycle fatigue (HCF) and very high cycle fatigue (VHCF) regimes. This differential equation possesses two main components: one is a stress function to accommodate the adopted fatigue criterion and the other one is a characteristic damage function that serves to capture the HCF response of alloys. Two distinct characteristic damage functions with three different multiaxial fatigue criteria, namely Sines, Crossland, and Dang Van criteria, are examined to develop six (out of many possible) variants of the presented damage accumulation model. As a validation measure, Chaboche's HCF damage model is retrieved as a specific case of the developed formalism. For model parameters identification, an ad hoc two-level identification scheme is designed and numerically verified. It is demonstrated that endurance limit, which is determined from fully reversed HCF tests (i.e., R = -1), can be identified from fatigue tests with positive stress ratio (R > 0), thus making our development quite suitable for specimens prone to buckling under compression. Another salient feature of the devised identification scheme is its capability in extracting model parameters from noisy data. [ABSTRACT FROM AUTHOR]

This article is concerned with the identification, from observations or field measurements, of the hydraulic conductivity for the saltwater intrusion problem in an unconfined aquifer. The involved model consists in a cross-diffusion system describing the evolutions of two interfaces: one between freshwater and saltwater and the other one between the saturated and unsaturated zones of the aquifer. The inverse problem is formulated as an optimization problem, where the cost function is a least square functional measuring the discrepancy between experimental interfaces depths and those provided by the model. Considering the exact problem as a constraint for the optimization problem and introducing the Lagrangian associated with the cost function, we prove that the optimality system has at least one solution. Moreover, we establish the first-order necessary optimality conditions. A numerical method is implemented to solve this identification problem. Some numerical results are presented to illustrate the ability of the method to determine the unknown parameters. [ABSTRACT FROM AUTHOR]

• The improved Stribeck model is used to establish the non-linear friction model of a electro-hydraulic system. • The improved genetic algorithm is used to identify four unknown parameters in the friction model. • A dynamic friction feedforward compensation method is designed based on the velocity observer and the structural invariance principle. • Effectiveness of the friction model and the friction feedforward compensation method is validated by experiment. It is necessary to identify and compensate the non-linear friction to improve the performances of the electro-hydraulic system of robotic excavators. The mathematical model of the electro-hydraulic system is established first, and the non-linear friction are described by the improved Stribeck model. The actual friction force is calculated by measuring the pressures of the two chambers of the hydraulic cylinder at different constant velocities. The fitness, crossover and mutation probabilities of the genetic algorithm are improved to overcome the shortcomings of stagnation and premature convergence. Four unknown parameters in the friction model are identified and verified by the improved genetic algorithm. According to the displacement signal from the sensor, a velocity observer is used to estimate the velocity signal in real time. Based on the principle of structural invariance, a dynamic friction feedforward compensation method is designed. On this basis, the experiments of friction compensation (FC) under three working conditions of sinusoidal trajectory, low and high velocities tracking were carried out for the arm electro-hydraulic system. The results show that the proposed Stribeck model and friction compensation control method effectively eliminates the low velocity crawling and amplitude flattening phenomena, and improves the control performances of the system. [ABSTRACT FROM AUTHOR]

In this work, the identification of the residual magnetic flux density of NdFeB permanent magnet, used in a drum magnetic separator, is performed. This identification problem has been formulated as an inverse optimization problem and solved using the Particle Swarm Optimization (PSO) method. For fast calculations, flux densities are computed using a 3D analytical method and the obtained results are compared with measurements. [ABSTRACT FROM AUTHOR]

Preisach model is widely used to characterize ferromagnetic material's hysteresis. Thus, it is necessary to identify its parameters in order to complete magnetic hysteresis modeling successfully. In this paper, a stochastic identification method, Genetic algorithm, is implemented. The procedure consists of the minimization of the error between measurements and program results. Obtained results are compared with the classical nonlinear least square identification method and experimental hysteresis of a fully-process non oriented Fe-3 wt%Si steel sheet. [ABSTRACT FROM AUTHOR]

In order to improve the tracking accuracy of a hydraulic system, an improved ant colony optimization algorithm (IACO) is proposed to optimize the values of proportional–integral–derivative (PID) controller. In addition, this paper presents an experimental study on the parameters identification to deduce accurate numerical values of the hydraulic system, which also determines the relationship between control signal and output displacement. Firstly, the basic principle of the hydraulic system and the mathematical model of the electro-hydraulic proportional control system are analyzed. Based on the theoretical models, the transfer function of the control system is obtained by recursive least square identification method (RLS). Then, the key parameters of the control system model are obtained. Some improvements are proposed to avoid premature convergence and slow convergence rate of ACO: the transition probability is revised based adjacent search mechanism, dynamic pheromone evaporation coefficient adjustment strategy is adopted, pheromone update rule and parameters optimization range are also improved. Then the proposed IACO tuning based PID controller and the identification parameters are modeled and simulated using MATLAB/Simulink and AMESim co-simulation platform. Comparisons of IACO, standard ACO and Ziegler–Nichols (Z–N)PID controllers are carried out with different references as step signal and sinusoidal wave using the co-simulation platform. The simulation results of the bucket system using the proposed controller demonstrates improved settling time, rise time and the convergence speed with a new objective function J. Finally, experiments with leveling operations are performed on a 23 ton robotic excavator. The experimental results show that the proposed controller improves the trajectory accuracy of the leveling operation by 28% in comparison to the standard ACO-PID controller. • The mathematical models of electro-hydraulic proportional system are described. • The key control system parameters are identified and then validated against experiments. • An improved ant colony optimization algorithm is proposed for achieving excellent control performances. • Co-simulation platform is carried out to estimate the performance of controller. • Effectiveness of the improved ant colony optimization algorithm is validated by experiment. [ABSTRACT FROM AUTHOR]

The novel contribution of this article is to propose a neural network–based sliding-mode control strategy for improving the position-control performance of a tendon sheath–actuated compliant rescue manipulator. Structural design of a rescue robot with slender and compliant mechanical structure is introduced. The developed robot is capable of drilling into the narrow space under debris and accommodating complicated configuration in ruins. Dynamics modeling and parameters identification of a compliant gripper with flexible tendon sheath transmission are researched and discussed. Moreover, the neural network–based sliding-mode control scheme developed based on radial basis function network is proposed to improve the position-control accuracy of the gripper with modeling uncertainties and external disturbances. The stability of the proposed control system is demonstrated using Lyapunov stability theory. Further experimental investigation including trajectory-tracking experiments and step-response experiments are conducted to confirm the effectiveness of the proposed neural network–based sliding-mode control scheme. Experimental results show that the proposed neural network–based sliding-mode control scheme is superior to cascaded proportional–integral–derivative controller and conventional sliding-mode controller in position-control application. [ABSTRACT FROM AUTHOR]

This paper investigates on the relationship between the arrangement of collagen fibers within soft tissues and parameters of constitutive models. Starting from numerical experiments based on biaxial loading conditions, the study addresses both the direct (from structure to mechanics) and the inverse (from mechanics to structure) problems, solved introducing optimization problems for model calibration and regression analysis. A campaign of parametric analyses is conducted in order to consider fibers distributions with different main orientation and angular dispersion. Different anisotropic constitutive models are employed, accounting for fibers dispersion either with a generalized structural approach or with an increasing number of strain energy terms. Benchmark data sets, toward which constitutive models are fitted, are built by employing a multiscale description of fiber nonlinearities and accounting for fibers dispersion with an angular integration method. Results show how the optimal values of constitutive parameters obtained from model calibration vary as a function of fibers arrangement and testing protocol. Moreover, the fitting capabilities of constitutive models are discussed. A novel strategy for model calibration is also proposed, in order to obtain a robust accuracy with respect to different loading conditions starting from a low number of mechanical tests. Furthermore, novel results useful for the inverse determination of the mean angle and the variance of fibers distribution are obtained. Therefore, the study contributes: to better design procedures for model calibration; to account for mechanical alterations due to remodeling mechanisms; and to gain structural information in a nondestructive way. [ABSTRACT FROM AUTHOR]

Stadium grandstands are designed to support high dynamic loads due to the presence of spectators attending both sport and music events. In order to assess accurately the dynamic response of these structures under these actions, spectator–grandstand interaction models have been widely considered. In spite of the wide range of different rhythmic activities, which can be performed by spectators on stadium grandstands, the modal parameters of these interaction models have been determined experimentally for only two extreme situations, the so-called passive and active spectators. In order to overcome this limitation, the variation of the modal parameters of a spectator–grandstand interaction model in terms of the rhythmic activities performed by the spectators was analyzed in this study. As parameters estimator, the ordinary least squares method was considered. The parameters identification has been performed based on the results of a full-scale experimental test conducted on a real grandstand. As result of the identification process, a clear variation of the modal parameters of the interaction model in terms of the activity type and rate has been detected. Therefore, this interaction model becomes a valuable tool for estimating the change on the modal properties of grandstands induced by the different rhythmic activities performed by the spectators. [ABSTRACT FROM AUTHOR]

Abstract A normalized Bouc-Wen model is adopt in the modelling to describe the hysteresis of magneto-rheological (MR) damper. An efficient parameters identification method of normalized Bouc-Wen model for MR damper is introduced in this paper. The parameters identification method of normalized Bouc-Wen model proposed in this paper is divided into three steps. Firstly, the physical meaning of normalized Bouc-Wen model are fully taken into consideration and the least-squares method is used to identify the parameters. Then, a Genetic Algorithm (GA) is utilized to identify the remaining parameters with an appropriate fitness function. The simulation results show that, compared to the other two method, the method proposed in this paper is more accurate, more efficient and less sensitive to data noise. [ABSTRACT FROM AUTHOR]

This paper studies parameters identification and minimizing base disturbances problems after the space manipulator capturing an unknown target. A concurrent learning algorithm that concurrently uses past motion data points and instantaneous motion data of the system is proposed for the parameters identification. Given a condition for selecting the used past data points as well as a scaling technique to make the parameters have the same magnitude, the concurrent learning algorithm guarantees that parameters identification errors can globally converge to zero at an exponential rate and without the need for satisfying the persistent excitation (PE) condition. An adaptive reactionless control method is proposed based on the passivity theorem and Task-priority method, which ensures that the base attitude is stationary and joint motions satisfy the limits during the system generating excitation motions for the parameters identification. Simulation results verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract Bearing reaction forces are commonly approximated by linear coefficients at a given rotational speed, although they are, in reality, considerably nonlinear in some situations. Therefore, a precise numerical simulation is highly desirable before experimental tests, usually resorting to integration of classical Reynolds equation, a well-known numerically time consuming. Due to faults or unusual demands, controllers can be applied to avoid failure. However, control action changes the machine dynamic behavior. This paper investigates the performance of a faster simulation method using a high order Taylor force approximation, by means of nonlinear coefficients, in situations that differs from the one in which the force was originally identified. This is accomplished using the force generated by a gain-scheduled static H ∞ controller. The proposed controller aims to stabilize fluid-induced instability and attenuate unbalance vibration at critical speed of a journal bearing supported rotor. A polynomial fit is applied to express the hydrodynamic bearings coefficients according to rotor rotational speed. The resultant polynomial system is then used in linear matrix inequalities from a two-stage method in order to synthetize a controller linearly dependent on rotational speed. Furthermore, the control efficiency is verified under the influence of bearing nonlinearities. The numerical simulations were compared with experimental data measured in a laboratory test rig. The results reveal that the control is effective in reduce vibration amplitudes especially in critical speed and fluid induced instability, and that the nonlinear coefficients are promising for approximating journal motion for both uncontrolled and controlled systems. [ABSTRACT FROM AUTHOR]

Highlights • PGJAYA is proposed to estimate the model parameters of PV cell and module. • Individual performance is quantified to assign update strategy to individual. • Self-adaptive chaotic perturbation is used to improve the population quality. • Experimental results indicate the superior performance of PGJAYA. Abstract In order to carry out the evaluation, control and maximum power point tracking on photovoltaic (PV) systems, accurate and reliable model parameter identification of PV cell and module is always desired. In this study, a performance-guided JAYA (PGJAYA) algorithm is proposed for extracting parameters of different PV models. In proposed PGJAYA algorithm, the individual performance in the whole population is quantified through probability. Then, based on probability, each individual can self-adaptively select different evolution strategies designed for balancing exploration and exploitation abilities to conduct the searching process. Meanwhile, the quantified performance is employed to select the exemplar to construct the promising searching direction. In addition, a self-adaptive chaotic perturbation mechanism is introduced around the current best solution to explore more better solution for replacing the worst one, thus improving the quality of whole population. The parameters estimation performance of PGJAYA is evaluated through three widely used standard datasets of different PV models including single diode, double diode, and PV module. Comparative and statistical results demonstrate that PGJAYA has a superior performance as it always obtains the most accurate parameters with strong robustness among all compared algorithms. Furthermore, the tests based on experimental data from the data sheet of different types of PV modules suggest that the proposed algorithm can achieve superior results at different irradiance and temperature. Based on these superiorities, it is concluded that PGJAYA is a promising parameter identification method for PV cell and module model. [ABSTRACT FROM AUTHOR]

Abstract To accurately describe the voltage performance of supercapacitor and estimate its energy, an equivalent circuit model with a controlled current source is proposed. First, the self-discharge effect of the supercapacitor is investigated based on experimental analysis at different charge and discharge stages. Furthermore, the controlled current source is utilized to reflect the self-discharge effect of the supercapacitor. The recursive least square method is introduced to identify the model parameters, and the particle swarm optimization algorithm is adopted to identify the parameters of the controlled current source at different stages. On this basis, the supercapacitor energy is estimated. Results show that the proposed model and the parameter identification method can effectively reveal the supercapacitor's voltage performance. Moreover, the proposed model provides more accurate energy estimation for the supercapacitor. [ABSTRACT FROM AUTHOR]

This investigation is concerned with optimal identification of the parameters in Sun-Chen's plasticity model. An improved off-axis tensile test scheme involving only two angles is proposed. The model's feasibility is assessed with a combined mathematical and mechanical analysis. In mathematical identification of a minimum of two angles, explicit derivation of the goodness of a power-law fitting on effective stress-plastic strain curves via least square theory was imperative. In mechanical analysis to identify the optimal angles, coupling between the resolved shear and transverse stresses in the evolutions of plastic deformation is investigated; the angle that generates a maximal plastic flow is calculated. In addition, a sensitivity analysis of identified parameters with the fiber's nominal angle errors is performed and the result is presented via an insensitivity path. Specimens from carbon/epoxy systems were utilized in the experimental program. Results confirm that the predictions of shear and off-axis nonlinear stress-strain responses based on the improved scheme (15° and 45°) proposed in this investigation demonstrate comparable accuracy to those based on Sun-Chen's common scheme. [ABSTRACT FROM AUTHOR]

Highlights • A new method is proposed to estimate the plastic properties of materials using spherical indentation and statistical Bayesian inference approach. • The set of material parameters identified by this method is not a single point, but a domain with probabilistic information. • The definition of 95% confidence interval provides a parameter bound that the estimates are located in this region with high probability. • Plastic properties of 20MnB5 steel extracted by indentation show good agreement with uniaxial data. Abstract In this paper, we proposed a novel approach to extract the plastic properties of materials using the loading curve (P-h) in spherical indentation experiment. The new method uses proper orthogonal decomposition to build the sub-space of indentation P-h snapshots, and then a statistical Bayesian inference model is derived based on the correlations between constitutive parameters and the sub-space coordinates of indentation P-h snapshots. Posterior sampling of material plastic parameters is performed using the Transitional Markov chain Monte Carlo (TMCMC) algorithm. The established new method has several advantages: 1). Bayesian model updating is performed in the sub-space of indentation P-h snapshots, of which the overall computation costs can be reduced greatly; 2). unlike most of the current analysis which can only give a single optimal estimation point of material properties, the present method can take account of the potential model and experiment uncertainties in indentation analysis. It's able to give the probabilistic information of the estimates, and provide a confidence interval (CI) of the inverse identified set of material properties under a certain confidence level, e.g. 95%. The effectiveness of the method is verified by its application on the 20MnB5 steel, of which two hardening laws, Hollomon and Ludwigson, are separately used. The unique solution of material plastic properties is obtained, and the parameters estimated by indentation and uniaxial experiment show good agreement. The influence of the selected hardening law and prescribed maximum indentation experiment depth on the numerical results are also investigated. Results indicate the new method is very reliable and effective. Besides, Ludwigson law gives more stable numerical results of 20MnB5. Graphical abstract Image, graphical abstract [ABSTRACT FROM AUTHOR]

In this study, we proposed an advanced control system for tilting-ladle-type automatic pouring machines in the casting industry. Automatic pouring machines have been introduced recently to improve the working environment of the pouring process. In the conventional study on pouring control, it has been confirmed that the pouring flow rate control contributes to improving the accuracy of the entire automatic pouring machine, such as the outflow liquid’s falling position from the ladle, the liquid’s weight filled in the mold, and the sprue cup’s liquid level. However, the conventional control system has problems: it is not easy to precisely pour the liquid in the ladle with a large tilting angle, and it takes time to adjust the control parameters. Therefore, we proposed the feedforward pouring flow rate control system, constructed by the pouring process’ inverse model with the online model parameters identification. In this approach, we derived the pouring process’ mathematical model, representing precisely the pouring process with the ladle’s large tilting angle. The model parameters in the pouring process’ inverse model in the controller are updated online via the model parameters identification. To verify the proposed pouring control system’s efficacy, we experimented using the tilting-ladle-type automatic pouring machine. In the experimental results, the mean absolute error between the outflow liquid’s weight and the reference weight was improved from 0.1346 at the first pouring to 0.0498 at the fifth pouring. Moreover, the model parameters were identified within 4 s. Therefore, it enables updating the controller’s parameters within each pouring motion interval by the proposed approach.

The inherent flexibility of hierarchical structure scheme with main-servo loop control structure is proposed to the problem of integrated chassis control system for the vehicle. It includes both main loop, which calculates and allocates the aim force using the optimal robust control algorithm and servo loop control systems, which track and achieve the target force using the onboard independent brake actuators. In fact, for the brake actuator, the aim friction is obtained by tracking the corresponding slip ratio of target force. For the coefficient of tire-road friction varying with different road surface, to get the nonlinear time-varying target slip ratio, the most famous quasi-static magic formula is proposed to estimate and predict real-time coefficient of different road surface and the constrained hybrid genetic algorithm (GA) is used to identify the key parameters of the magic formula on-line. Then, a self-tuning longitudinal slip ratio controller (LSC) based on the nonsingular and fast terminal sliding mode (NFTSM) control method is designed to improve the tracking accuracy and response speed of the actuators. At last, the proposed integrated chassis control strategies and the self-tuning control strategies are verified by computer simulations.

The present paper is focused on the post processing of the data coming from the Blade Tip-Timing (BTT) sensors in the case where two very close peaks are present in the frequency response of the vibrating system. This type of dynamic response with two very close peaks can occur quite often in bladed disks. It is related to the fact that the bladed disk is not perfectly cyclic symmetric and the so called “mistuning” is present. A method based on the fitting of the BTT sensors data by means of a 2 degrees of freedom (2DOF) dynamic model is proposed. Nonlinear least square optimization technique is employed for identification of the vibration characteristics. A numerical test case based on a lump parameter model of a bladed disk assembly is used to simulate different response curves and the corresponding sensors signals. The Frequency Response Function (FRF) constructed at the resonance region is compared with the traditional Sine fitting results, the resonance frequencies and damping values estimated by the fitting procedure are also reported. Accurate predictions are achieved and the results demonstrate the considerable capacity of the 2DOF method to be used as a standalone or as a complement to the standard Sine fitting method.

This paper proposes an approach to develop building dynamic thermal models that are of paramount importance for controller application. In this context, controller requires a low-order, computationally efficient, and accurate models to achieve higher performance. An efficient building model is developed by having proper structural knowledge of low-order model and identifying its parameter values. Simplified low-order systems can be developed using thermal network models using thermal resistances and capacitances. In order to determine the low-order model parameter values, a specific approach is proposed using a stochastic particle swarm optimization. This method provides a significant approximation of the parameters when compared to the reference model whilst allowing low-order model to achieve 40% to 50% computational efficiency than the reference one. Additionally, extensive simulations are carried to evaluate the proposed simplified model with solar radiation and identified model parameters. The developed simplified model is afterward validated with real data from a case study building where the achieved results clearly show a high degree of accuracy compared to the actual data.

The open circuit voltage (OCV) and model parameters are critical reference variables for a lithium-ion battery management system estimating the state of charge (SOC) accurately. However, the polarization effect reduces the accuracy of the OCV test, and the model parameters coupled to the polarization voltage increase the non-linearity of the cell model, all challenging SOC estimation. This paper presents an OCV curve fusion method based on the incremental and low-current test. Fusing the incremental test results without polarization effect and the low current test results with non-linear characteristics of electrodes, the fusion method improves the OCV curve’s accuracy. In addition, we design a state observer with model parameters and SOC, and the unscented Kalman filter (UKF) method is employed for co-estimation of SOC and model parameters to eliminate the drift noise effects. The SOC estimation root mean square error (RMSE) of the proposed method achieves 0.99% and 1.67% in the pulse constant current test and dynamic discharge test, respectively. Experimental results and comparisons with other methods highlight the SOC estimation accuracy and robustness of the proposed method.

The identification of values of solar cell parameters is of great interest for evaluating solar cell performances. The algorithm of an artificial bee colony was used to extract model parameters of solar cells from current-voltage characteristics. Firstly, the best-so-for mechanism was introduced to the original artificial bee colony. Then, a method was proposed to identify parameters for a single diode model and double diode model using this improved artificial bee colony. Experimental results clearly demonstrate the effectiveness of the proposed method and its superior performance compared to other competing methods.

Graphical abstract Highlights • The paper proposes an efficient SSA-based approach to extract the PV cell parameters. • SSA is firstly compared to the novel SCA and VCS optimizers not used before. • SSA is then compared with well-established optimizers such as ALO, GSA, and WOA. • Experimental tests are also performed to ensure the effectiveness of SSA. • Comparisons and metrics support the experimental results and show SSA efficacy. Abstract Solar Photovoltaic systems (SPVSs) are becoming one of the most popular renewable energy technology for generating significant share of electric power. With the consistent growth of SPVSs applications, the challenge of parameters estimation of photovoltaic cells has drawn the attention of researchers and industrialists and gained immense momentum for SPVSs modeling. This paper proposes an efficient approach based on Salp Swarm Algorithm (SSA) for extracting the parameters of the electrical equivalent circuit of PV cell based double-diode model. The experimental and comparative results demonstrate that SSA is highly competitive with the results of two algorithms that have never been used before for the PV cell parameter extraction namely Sine Cosine Algorithm (SCA) and Virus Colony Search Algorithm (VCS). SSA is also significantly better than three well-established parameter extraction algorithms namely Ant Lion Optimizer (ALO), Gravitational Search Algorithm (GSA) and Whale Optimization Algorithm (WOA). Several evaluation criteria including Mean Square Error (MSE), Absolute Error (AE) and statistical criterion show that the SSA algorithm provides the highest value of accuracy and has merits in designing SPVSs. [ABSTRACT FROM AUTHOR]

Considering the particular demands for a large damping force in special vehicles, a new design for a parallel disc slotted magnetorheological damper is presented. In this damping generator, the direction of liquid flow between disc gaps is completely perpendicular to the magnetic flux direction, and the gaps can be cascaded to enhance the damping effect, thereby introducing a larger damping force based on the magnetorheological effect. In this paper, the damping characteristics of the new damper design are experimentally analysed first. Based on the experimental results, a dynamic model for the damper is constructed. The mathematical model is obtained by combining the Bingham model and the Herschel-Bulkley model, and accurately and clearly describes the characteristics of magnetorheological fluid. For parameter identification, two methods are utilized: a trial-and-error method and a recursive least squares method. Analysis shows that both methods have drawbacks. Lastly, a new method is proposed for combining the trial-and-error method with recursive least squares method. By comparing the theoretical curves obtained from the experimental curves and combined methodology, the results show that the combined methodology exhibits the highest accuracy as well as efficiency. The mathematical model established in this paper can accurately describe the characteristics of damping force. This model lays down the theoretical foundations for application and research on new parallel disk slotted magnetorheological dampers. [ABSTRACT FROM AUTHOR]

Highlights • Identification of constitutive model parameters for elasto-plastic fracture. • Multiscale experimental testing of silver-plated copper busbars for photovoltaics. • Identification based on the gradient descent method and particle swarm optimization. Abstract Silver-plated copper busbars are screen printed onto silicon solar cells and have the key role to collect the electric current produced by the solar cell. Busbars of two adjacent solar cells are then connected by a soldered ribbon made of the same material. Due to mechanical and thermal loads, such a ribbon is subject to axial deformation that, often, causes plasticity and, in some cases, its breakage due to crack growth. A procedure based on the gradient-descent method and particle swarm optimization is herein proposed for the identification of elasto-plastic and nonlinear (cohesive zone model, CZM) fracture mechanics parameters of silver-plated copper busbars. The proposed method requires the experimental determination of the force-displacement curves from uniaxial tensile tests on busbar samples with and without initial notches. The inspection of in situ SEM images during the tests allows also the estimation of the crack opening, which is found to be an important local quantity to assess the reliability of different CZMs in simulating a crack growth process consistent with the real one. [ABSTRACT FROM AUTHOR]

Parametric analysis of in-plane vibration characteristic for unloaded heavily loaded radial tire is put forward and researched utilizing in-plane rigid-elastic coupled model. Coupled vibration characteristic between the flexible tread and circumferential sidewall is investigated with theoretical modeling and experimental modal method. In-plane analytical vibration feature is modeled with flexible beam on modified elastic foundation tire model. The rigid-elastic coupled tire model is derived with finite difference method and stiffness matrix and mass matrix are presented analytically with the geometrical and structural parameters. Structural parameters identification is implemented with genetic algorithm based on in-plane experimental modal parameter. The in-plane transfer functions with different structural parameters are compared and the parametric effect of structural parameters on in-plane transfer function is discussed. Experimental and theoretical result shows that the in-plane rigid-elastic coupled tire model can achieve the higher precision on predicting the transfer function and vibration feature of heavily loaded tire within the frequency band of 300 Hz. [ABSTRACT FROM AUTHOR]

In this paper, a robust control scheme to drive a knee‐exoskeleton has been designed to assist people with limited knee movement. Dynamic modeling and parameter identification of the "subject's lower limb‐exoskeleton" has been performed and a sliding mode observer (SMO) developed and integrated in the system's closed loop to provide velocity. Lyapunov's theory allows demonstration of the stability of the system. Experimental tests have been performed with the aid of five voluntary subjects, in sitting position, and during flexion/extension of the knee joint. The performance of the proposed control scheme is compared with those obtained using the classical proportional integrator derivative (PID) controller. These tests track the desired position and velocity trajectories with small tracking errors and evaluate the system's stability and system's robustness against the subject's parameters, variations, and external disturbances. [ABSTRACT FROM AUTHOR]

Many practical systems, such as thermal system, economic system and electric power system, can be more accurately described by the fractional-order system rather than integer-order system. Therefore, it is an important topic to study the fractional-order system and estimate its parameters. The problem of parameter estimation is essentially a multi-dimensional parameter optimization problem. In this paper, according to the average value of position information, an improved Tent mapping and a piecewise mutation probability, a modified particle swarm optimization (MPSO) algorithm is presented to solve the parameter estimation problem. The performance of MPSO is tested with eight benchmark functions, which proves the effectiveness of the algorithm. Based on the double-dispersion Cole model, the proposed MPSO algorithm is used to estimate the parameters for the generated simulated datasets. Experimental results show that the MPSO algorithm for parameters identification of the Cole model is an effective and promising method with high accuracy and good robustness. [ABSTRACT FROM AUTHOR]

Highlights • A novel model and measurement method for contact analysis of TWUSM are proposed. • The proposed measurement method make it possible to determine motor parameters in running TWUSM. • Vibration parameters in our model are identified based on measured force and feedback voltage rather than theoretical assumption. • The simultaneous effects of the preload force on the resonance frequencies and vibration amplitudes were validated rather than one effect. • Some phenomena, such as the variations of starting-up voltages and working ranges can be predicted and analyzed. • Very useful and convenient for the motor design, performance prediction, working range choice and control of TWUSM. Abstract In previous studies, contact models of traveling-wave ultrasonic motor (TWUSM) only considered the effect of the preload on the resonance frequency, and were based on theoretical calculations or assumptions because vibration parameters in working conditions are hard to measure. In this paper, a novel model and measurement method for contact analysis of TWUSM are proposed. The proposed measurement method enables the determination of vibration parameters during motor operation. Our model differs from previous reported models in three aspects: vibration parameters are identified based on measured force and feedback voltage rather than theoretical assumptions; the simultaneous effects of the preload force on the resonance frequency and vibration amplitude were analyzed and validated; some phenomena in working conditions, such as the starting-up voltages and working ranges under different preload forces, can be predicted and analyzed. The feasibility and effectiveness of this model was verified by the good agreement between the measured and calculated results. Our work provides a very useful and convenient way for performance prediction, working range choice and control of TWUSM. [ABSTRACT FROM AUTHOR]

Abstract Magnetic properties of ferromagnetic materials are sensitive to mechanical deformation, temperature stress and frequency of excitation. This paper deals with the characterization of the behavior of ferromagnetic materials under plastic deformation. Our work attempts to take into account the plastic deformation in the analytical Jiles-Atherton model through its parameters: their variations with the mechanical deformation are deduced from an identification using genetic algorithm approach for each state of deformation. Sensitive parameters to the solicitation are extended to plastic deformation. Finally, simulation results are compared to experimental data of a fully process non oriented Fe-3 wt%Si steel sheet. The mean square error is less than 5% for each case which confirms the good agreement between simulated and measured hysteresis curves. [ABSTRACT FROM AUTHOR]

With the wind power energy largely interconnected with power grid, it is necessary to develop a robust dynamic equivalent model of the wind farm for the system stability analysis and control. In this paper, a detailed wind farm model with doubly fed induction generators is established according to Western Electricity Coordinating Council benchmark test system. Then, the parameters identification process based on aggregation technique consisting of a hybrid algorithm combined improved-particle swarm optimization with improved-genetic algorithm is proposed for dynamic equivalent model development by using the measurement data from phasor measurement unit at the point of interconnection. Additionally, the robustness of the developed equivalent model is analyzed. The simulation results of the benchmark test system and IEEE-9 bus system demonstrate the effectiveness and robustness of the proposed equivalent modeling and identification methodology. [ABSTRACT FROM AUTHOR]

An adaptive hybrid differential evolution algorithm for the dynamic parameter identification of rotating machinery is developed in this paper. Genetic algorithm (GA) is first used to reduce the range of the identification parameters to save computational time. Adaptive Cauchy and Gaussian mutations are then applied in a differential evolution algorithm (DEA) and dynamically updated during the evolutionary process according to the identification process, to obtain the global optimal solution more easily and more rapidly. Numerical studies of linear and nonlinear rotor-bearing systems are performed. The results indicate that the genetic algorithm-adaptive hybrid differential evolution algorithm (GA-AHDE) is more effective at identifying the dynamic parameters and faults of the rotor-bearing system compared to other evolutionary algorithms. [ABSTRACT FROM AUTHOR]

This study presents an approach to build a reduced‐order model (ROM) for the governor control systems of diesel‐engine generators in an island power system. The hybrid particle swarm optimisation (PSO) is used in the parameter identification of the ROM. The reduced‐order governor system model could be a useful and feasible model in the stability analysis of the island power system by using power system simulator for engineering. The results of the ROM and a sixth‐order model have been compared. It is found that the ROM with the parameter values identified using the hybrid PSO is robust. Moreover, real‐case validation of the ROM shows that it is usable to analyse stability and contingency in the power system. [ABSTRACT FROM AUTHOR]

In this brief, a strategy is proposed for the identification, starting from experimental measurements on piezoelectric actuators, of all the parameters of the recently proposed power-law hysteresis and creep model. The algorithm allows trading off modeling accuracy and speed of the inverse model (compensator) implemented in a digital device. An analysis of the relationships among parameters and their effects on modeling and circuit performances is also carried out. The proposed strategy is tested on two piezoelectric actuators, showing that the obtained models are able to fulfill the circuit specifications. [ABSTRACT FROM AUTHOR]

In this work, the Pachycondyla Apicalis metaheuristic algorithm (API) is used to identify and optimize control parameters for piezoelectric oscillator that exhibits frequency hysteresis behavior under strong excitation when asymmetric period which the bifurcation and chaotic behavior of higher harmonics appear by minimizing errors between actual and evaluated states of the model. In order to investigate the efficiency of the API algorithm, numerical experiments are carried out on the piezoelectric chaotic resonator. The simulation results indicate that the API algorithm can be effective in identifying the unknown parameters for given chaotic systems with high accuracy and low deviations. [ABSTRACT FROM AUTHOR]

We introduce the artificial fish swarm algorithm for heading motion model identification and control parameter optimization problems for the “Ocean Rambler” unmanned wave glider (UWG). First, under certain assumptions, the rigid-flexible multi-body system of the UWG was simplified as a rigid system composed of “thruster + float body”, based on which a planar motion model of the UWG was established. Second, we obtained the model parameters using an empirical method combined with parameter identification, which means that some parameters were estimated by the empirical method. In view of the specificity and importance of the heading control, heading model parameters were identified through the artificial fish swarm algorithm based on tank test data, so that we could take full advantage of the limited trial data to factually describe the dynamic characteristics of the system. Based on the established heading motion model, parameters of the heading S-surface controller were optimized using the artificial fish swarm algorithm. Heading motion comparison and maritime control experiments of the “Ocean Rambler” UWG were completed. Tank test results show high precision of heading motion prediction including heading angle and yawing angular velocity. The UWG shows good control performance in tank tests and sea trials. The efficiency of the proposed method is verified. [ABSTRACT FROM AUTHOR]