Identification Of Model Parameters Research Articles

A Novel Approach to Quantify Sediment Transfer and Storage in Rivers—Testing Feldspar Single‐Grain pIRIR Analysis and Numerical Simulations

AbstractQuantifying sediment transport and storage in fluvial systems is fundamental for understanding the transfer of eroded materials from source to sink. Here, we investigated the potential of a combined approach using a luminescence measured in modern fluvial sediments and numerical simulations to quantify transport and storage in rivers. We acquired luminescence data at the single‐grain scale on feldspar sediments of Waimakariri and Rakaia braided rivers (New Zealand) using a post infra‐red infra‐red stimulated luminescence protocol. We considered three metrics from the analysis of luminescence equivalent dose (De) distribution: the percentage of bleached and the percentage of saturated grains, and the meanDe. All three metrics show similar longitudinal trends along both rivers. To derive quantitative information on sediment transfer from these data, we developed a numerical model that simulates the longitudinal evolution ofDedistribution of a population of grains during transport and storage in floodplains. The model considers four main parameters: the transport length of grains during floods, their resting time between successive transport events, the bleaching probability (probability of the luminescence signal to be reset due to sunlight exposure during transport or rest), and the likelihood of partial bleaching. Identification of model parameters that best reproduce natural observations allowed estimating transit time of sediments between 2.1 and 10.3 Kyr (mean of 6.9 2.9 Kyr), corresponding to mean virtual transit velocity of 20–95 m.yr−1(mean of 46 28 m.yr−1). Our study illustrates the potential of the combined single‐grain luminescence and modeling approach to quantify sediment transfer in fluvial systems.

An Improved Capacity-Loss Diagnostic Model Based on Long Short-Term Memory Network

Due to the capacity-loss of lithium-ion batteries is caused by the coupling effect of multiple mechanisms, the online capacity-loss diagnosis is still a challenge, and diagnosing the capacity-loss by using the theoretical model needs considerable time and cost. To solve the above problems, an improved model for online capacity-loss diagnosis based on long short-term memory neural networks (LSTM) is proposed. The network architecture of the model is designed based on the modeling process of the theoretical model. The powerful ability of feature extraction of LSTM network is utilized to solve the problem of model parameter identification, and the network architecture can reduce the dependence of the model on training data to some extent. The verification results show that, compared with the unidentified theoretical model, the proposed model improves the accuracy of capacity-loss diagnosis by 2% by training the model, and it has better adaptability to different batteries.

State space model of undrained triaxial test data for Bayesian identification of constitutive model parameters

Soil constitutive model parameters can be identified from triaxial test data. The identification is frequently performed by fitting a constitutive model to triaxial test data from a purely statistical or probabilistic perspective based on an assumption that measurements are independent. This ignores the sequential attribute of triaxial test data and is, hence, not realistic. In this paper, a probabilistic state space model (SSM) is proposed for undrained triaxial test data analysis, with which the sequential data attribute is explicitly considered and a constitutive model (physics) is linked to the SSM model (statistics) in a natural way. Then, constitutive model parameters can be rigorously learned under a Bayesian framework based on the SSM without artificially augmenting them into hidden variables of SSM. Without loss of generality, the modified Cam Clay (MCC) model is taken as an example to develop the SSM and to demonstrate the proposed Bayesian framework, which is illustrated using simulated and real-life data. Results from the proposed Bayesian framework based on the SSM include not only the best estimates of MCC model parameters but also their posterior distributions for quantifying the identification uncertainty, based on which the identifiability of MCC model parameters is discussed and highlighted.

Dynamical Soft Sensors from Scarce and Irregularly Sampled Outputs Using Sparse Optimization Techniques

In process industries, quality variables such as concentrations and viscosity usually require offline laboratory analysis due to difficulties associated with online sensing and are often sampled slowly or irregularly compared to other variables such as temperatures and flow rates. Dynamical soft sensors, which relate the uniformly fast sampled variables to irregularly sampled quality variables, are crucial in control and process monitoring applications. Most identification approaches for soft sensing assume that all the variables are sampled regularly and uniformly. The existing auxiliary model (AM)-based approaches for dealing with irregular sampling suffer from drawbacks such as nonconvex optimization, lack of model parsimony and require prior information about the system dynamics. This work addresses a few of these issues by developing a flexible AM that can accommodate complex linear process dynamics without assuming any prior knowledge and compromising on model parsimony by using redundant Laguerre filters and casting the model learning in the sparse optimization framework. The developed AM is utilized to efficiently reconstruct the measurements at the base sampling interval, which further serves as a foreground for the traditional parametric model identification techniques or the expectation maximization algorithm to obtain optimal parameter estimates. The efficacy of the proposed AM-based soft sensor algorithm is demonstrated through synthetic as well as industrial simulation case studies. Finally, a few guidelines on effective sampling of quality variables to generate informative experiments for soft sensor development are also presented.

A two-stage method for model parameter identification based on the maximum power matching and improved flow direction algorithm

Parameter identification of the photovoltaic (PV) model is essential to research in the PV field. A two-stage method of model parameter identification based on maximum power matching (MPM) and improved flow direction algorithm (IFDA) is proposed. The two-stage method, i.e., MPM-based rough extraction and IFDA-based precise identification. The quality of I–V data dramatically impacts the PV model’s parameter identification accuracy. At first, the measured I–V curves are preprocessed. The process includes outlier removal, curve fitting, and sparsification of I–V data. Then the MPM is used for the rough extraction of model parameters from the preprocessed I–V data. Finally, the rough extraction results are used as the initial values of the iterations in the precise identification using IFDA. The root mean square error (RMSE) between the measured and calculated currents is used as the fitness function of IFDA. The experimental section compares sixteen methods. IFDA presents the highest accuracy with the smallest RMSE at 0.0024 A. Five methods with outstanding performance are selected for 1000 repetitive experiments. The IFDA is the most stable with RMSE in the range of 0.002 A to 0.003 A.

Online Identification of Lithium-ion Battery Model Parameters with Initial Value Uncertainty and Measurement Noise

Online parameter identification is essential for the accuracy of the battery equivalent circuit model (ECM). The traditional recursive least squares (RLS) method is easily biased with the noise disturbances from sensors, which degrades the modeling accuracy in practice. Meanwhile, the recursive total least squares (RTLS) method can deal with the noise interferences, but the parameter slowly converges to the reference with initial value uncertainty. To alleviate the above issues, this paper proposes a co-estimation framework utilizing the advantages of RLS and RTLS for a higher parameter identification performance of the battery ECM. RLS converges quickly by updating the parameters along the gradient of the cost function. RTLS is applied to attenuate the noise effect once the parameters have converged. Both simulation and experimental results prove that the proposed method has good accuracy, a fast convergence rate, and also robustness against noise corruption.

Parametric model identification of delta wing UAVs using filter error method augmented with particle swarm optimisation

AbstractFrom arsenal delivery to rescue missions, unmanned aerial vehicles (UAVs) are playing a crucial role in various fields, which brings the need for continuous evolution of system identification techniques to develop sophisticated mathematical models for effective flight control. In this paper, a novel parameter estimation technique based on filter error method (FEM) augmented with particle swarm optimisation (PSO) is developed and implemented to estimate the longitudinal and lateral-directional aerodynamic, stability and control derivatives of fixed-wing UAVs. The FEM used in the estimation technique is based on the steady-state extended Kalman filter, where the maximum likelihood cost function is minimised separately using a randomised solution search algorithm, PSO and the proposed method is termed FEM-PSO. A sufficient number of compatible flight data sets were generated using two cropped delta wing UAVs, namely CDFP and CDRW, which are used to analyse the applicability of the proposed estimation method. A comparison has been made between the parameter estimates obtained using the proposed method and the computationally intensive conventional FEM. It is observed that most of the FEM-PSO estimates are consistent with wind tunnel and conventional FEM estimates. It is also noticed that estimates of crucial aerodynamic derivatives ${C_{{L_\alpha }}},\;{C_{{m_\alpha }}},\;{C_{{Y_\beta }}},\;{C_{{l_\beta }}}$ and ${C_{{n_\beta }}}$ obtained using FEM-PSO are having relative offsets of 2.5%, 1.5%, 6.5%, 3.4% and 7.6% w.r.t. wind tunnel values for CDFP, and 1.4%, 1.9%, 0.1%, 9.6% and 7.5% w.r.t. wind tunnel values for CDRW. Despite having slightly higher Cramer-Rao Lower Bounds of estimated aerodynamic derivatives using the FEM-PSO method, the simulated responses have a relative error of less than 0.10% w.r.t. measured flight data. A proof-of-match exercise is also conducted to ascertain the efficacy of the estimates obtained using the proposed method. The degree of effectiveness of the FEM-PSO method is comparable with conventional FEM.

Parametric identification of Timoshenko-beam model for shear-wall structures using monitoring data

The feasible modeling of the shear-flexure coupling behavior of a shear wall is desired and needed for long-term structural health monitoring of shear-wall structures. To address this issue, a Timoshenko-beam finite-element model is proposed for referencing a shear-wall structure. A parametric identification framework along with a particle swarm optimization is developed for updating the target model using the vibration data. Numerical examples are analyzed with particular focuses on the selection of objective functions, and the potential effects of ground motions, sensor allocation, model selection, and measurement noise. Results indicate the preference of adopting the acceleration-based errors as the objective function, the robustness of identified optimal parameters regarding ground motion and sensor allocation, and the importance of choosing an appropriate model for referencing the structure. The accuracy of the proposed model is further validated using the data obtained from a Walnut Creek building under eight earthquakes from years 2015 to 2021. The averages of normalized root-mean-square acceleration prediction errors of the proposed model, along the longer and the shorter sides of the building, are 0.480 and 0.767, respectively. Comparisons with the shear-beam model, the Euler-Bernoulli-beam model, and the parallel connection of these two models further show that the Timoshenko-beam model outperforms other candidate models in predicting the response of the examined building.

A Novel Markov Model-Based Traffic Density Estimation Technique for Intelligent Transportation System

An intelligent transportation system (ITS) aims to improve traffic efficiency by integrating innovative sensing, control, and communications technologies. The industrial Internet of things (IIoT) and Industrial Revolution 4.0 recently merged to design the industrial Internet of things-intelligent transportation system (IIoT-ITS). IIoT sensing technologies play a significant role in acquiring raw data. The application continuously performs the complex task of managing traffic flows effectively based on several parameters, including the number of vehicles in the system, their location, and time. Traffic density estimation (TDE) is another important derived parameter desirable to keep track of the dynamic state of traffic volume. The expanding number of vehicles based on wireless connectivity provides new potential to predict traffic density more accurately and in real time as previously used methodologies. We explore the topic of assessing traffic density by using only a few simple metrics, such as the number of surrounding vehicles and disseminating beacons to roadside units and vice versa. This research paper investigates TDE techniques and presents a novel Markov model-based TDE technique for ITS. Finally, an OMNET++-based approach with an implementation of a significant modification of a traffic model combined with mathematical modeling of the Markov model is presented. It is intended for the study of real-world traffic traces, the identification of model parameters, and the development of simulated traffic.

Physics-based modelling of robot’s gearbox including non-linear phenomena

ABSTRACT Digital Twin is a industry 4.0 technology highly deployed in control, safety and maintenance . Hence, increasing the digital twins’ accuracy creates added value for these concepts. The digital representation of a robot requires the simulation of its dynamic behaviour. The friction torque in robot gearboxes seems to be a high considerable parameter in simulating robot dynamics. This paper presents a framework for representing the friction torque in industrial robot gearboxes. Initially, a literature review of the friction modelling approaches is conducted. The literature analysis indicates the LuGre model as an efficient approach for the description of the non-linear behaviour of the gearbox. The proposed procedure consists of a) the digital representation of the robot gearbox including LuGre friction model, b) the identification of LuGre model parameters with a metaheuristic Genetic Algorithm c) the validation of the proposed methodology using a 6-axis industrial robot. The digital representation of the robot gearbox is executed in OpenModelica. The LuGre parameters’ estimation is achieved by defining and solving an optimization problem in Matlab and the utilization of data gathered from robot controller. The validation is based on comparing the torque response of digital and real word model for a specific position signal.

Indirect adaptive fuzzy finite time synergetic control for power systems

Introduction. Budget constraints in a world ravenous for electrical power have led utility companies to operate generating stations with full power and sometimes at the limit of stability. In such drastic conditions the occurrence of any contingency or disturbance may lead to a critical situation starting with poorly damped oscillations followed by loss of synchronism and power system instability. In the past decades, the utilization of supplementary excitation control signals for improving power system stability has received much attention. Power system stabilizers (PSS) are used to generate supplementary control signals for the excitation system in order to damp low-frequency oscillations caused by load disturbances or short-circuit faults. Problem. Adaptive power system stabilizers have been proposed to adequately deal with a wide range of operating conditions, but they suffer from the major drawback of requiring parameter model identification, state observation and on-line feedback gain computation. Power systems are nonlinear systems, with configurations and parameters that fluctuate with time that which require a fully nonlinear model and an adaptive control scheme for a practical operating environment. A new nonlinear adaptive fuzzy approach based on synergetic control theory which has been developed for nonlinear power system stabilizers to overcome above mentioned problems. Aim. Synergetic control theory has been successfully applied in the design of power system stabilizers is a most promising robust control technique relying on the same principle of invariance found in sliding mode control, but without its chattering drawback. In most of its applications, synergetic control law was designed based on an asymptotic stability analysis and the system trajectories evolve to a specified attractor reaching the equilibrium in an infinite time. In this paper an indirect finite time adaptive fuzzy synergetic power system stabilizer for damping local and inter-area modes of oscillations for power systems is presented. Methodology. The proposed controller design is based on an adaptive fuzzy control combining a synergetic control theory with a finite-time attractor and Lyapunov synthesis. Enhancing existing adaptive fuzzy synergetic power system stabilizer, where fuzzy systems are used to approximate unknown system dynamics and robust synergetic control for only providing asymptotic stability of the closed-loop system, the proposed technique procures finite time convergence property in the derivation of the continuous synergetic control law. Analytical proofs for finite time convergence are presented confirming that the proposed adaptive scheme can guarantee that system signals are bounded and finite time stability obtained. Results. The performance of the proposed stabilizer is evaluated for a single machine infinite bus system and for a multi machine power system under different type of disturbances. Simulation results are compared to those obtained with a conventional adaptive fuzzy synergetic controller.

Composite Adaptive Synchronous Control of Dual-Drive Gantry Stage With Load Movement

In gantry systems, load usually moves according to different production tasks. The continuous change of the beam centroid position is a non-negligible factor for achieving high-performance synchronous control. Therefore, it cannot be simply treated as an external disturbance. In addition, advanced controller design and model parameter identification are very important for precision motion control systems. This article addresses the synchronous control of dual-drive gantry platforms with load movement to improve their steady-state and transient performance. Based on the analysis of the rigid and flexible characteristics of gantry mechanical components, a rigid-flexible coupling dynamic model is proposed, which provides a theoretical basis for the control system design. To ensure zero steady-state tracking error and parameter convergence in the presence of parameter uncertainties and unknown disturbances, a composite adaptive control method integrating excellent output tracking performance and parameter estimation is presented. Furthermore, the bridge between load movement and centroid position is established through frequency domain identification, which facilitates reasonable thrust distribution. Comparative experimental results are presented that confirm the effectiveness and advantages of the proposed synchronous control scheme.

Model Parameter Identification via a Hyperparameter Optimization Scheme for Autonomous Racing Systems

In this letter, we propose a model parameter identification method via a hyperparameter optimization scheme (MI-HPO). Our method adopts an efficient exploreexploit strategy to identify the parameters of dynamic models in a data-driven optimization manner. We utilize our method for model parameter identification of the AV-21, a full-scaled autonomous race vehicle. We then incorporate the optimized parameters for the design of modelbased planning and control systems of our platform. In experiments, MI-HPO exhibits more than 13 times faster convergence than traditional parameter identification methods. Furthermore, the parametric models learned via MIHPO demonstrate good fitness to the given datasets and show generalization ability in unseen dynamic scenarios. We further conduct extensive field tests to validate our model-based system, demonstrating stable obstacle avoidance and high-speed driving up to 217km/h at the Indianapolis Motor Speedway and Las Vegas Motor Speedway. The source code for our work and videos of the tests are available at https://github.com/hynkis/MI-HPO.

The practical rules for aircraft parameters identification based on flight test data

<abstract><p>The mathematical analysis of the problem of irregularity of estimates inherent in the problem of parametric identification of aircraft mathematical models according to flight experiments data is carried out. It is shown that to ensure the reliability of identification results, usually it is not enough to use only observations of the input and output signals of the object, but it is necessary to attract additional information taken from the basic scientific and engineering disciplines related to the subject under study, and apply other special techniques. For the task of estimating the parameters of aircraft models using flight experiment data, the basic rules for performing identification are formulated.</p></abstract>

METHODS OF MATHEMATICAL SIMULATION AND MACHINE IDENTIFICATION OF ANOMALOUS DIFFUSION PROCESSES

For the class of anomalous diffusion processes, the mathematical models of which are formalized in the form of variational inequalities in partial derivatives, a method of mathematical modeling based on the optimization procedure is proposed. The method is considered in relation to the generalized mathematical model of the studied class of anomalous diffusion processes. Which made it possible to ensure the principle of unification and typification in the application of this method, as well as the correctness of using the generalized mathematical model in applied problems of mathematical modeling of known industrial and practically important natural cases of anomalous diffusion processes. At the same time, the task of implementing mathematical models of anomalous diffusion processes based on the proposed method is reduced to finding the maximum of the Hamiltonian function defined in the state space of the processes under consideration. A method of parametric identification of mathematical models of anomalous diffusion processes in the formulation of the problem of optimal control is also proposed. The method is reduced to the use of the optimization procedure of the gradient projection method. The possibility of solving the problem of parametric identification in cases of both linear and non-linear mathematical models of anomalous diffusion processes is proved. Moreover, the nonlinear formulation of the parametric identification problem does not lead to computational implementation complications, since the solution is based only on finding the gradient projection of the state function of the anomalous diffusion process. The proposed methods are presented in strict compliance with the provisions of functional analysis, which ensures their correctness and adequacy in solving a wide range of applied problems.

Global Sensitivity Analysis of 0-D Lithium Sulfur Electrochemical Model

This paper examines global parameter sensitivity in a zero-dimensional lithium-sulfur (Li-S) battery model. Li-S batteries are an appealing cell chemistry due to their high theoretical energy density, abundant supply, and low cost. Due to the lack of complete understanding of the underlying working mechanisms for Li-S cells, the development of mathematical models and state estimation is still in its early stages. Model development and parameter Identification are closely associated. Both are essential for developing battery management systems (BMS) in commercialized Li-S powered applications. This paper highlights the parameter Identification challenges associated with Li-S models. Sensitivity analysis helps for parameter Identification by revealing information about parameter relevance and interdependence. Sensitivity analysis also helps in understanding different physical ranges of parameters, and their impact on model performance/response. In this work, we examine global sensitivity analysis (GSA). A key challenge for GSA, especially in Li-S batteries, is a lack of available information in the literature about a-priori distributions of Li-S parameters. This paper aims to elucidate this challenge by comparing GSA under different parameter distributions. Three model parameters are chosen in this analysis, and their sensitivities are compared under three different distributions. We find that, under certain distributions of parameters, relative importance of the parameters shifts.

Mathematical modeling of vibration drying process taking into account the longitudinal movement of grain material

Purpose. Construction of a mathematical model of non-stationary thermal mass transfer in the drying process in the vibration equipment, taking into account the longitudinal movement of the grain material. Methods. Mathematical modeling methods based on a formalized description in the form of two nonlinear inhomogeneous differential equations of the non-stationary process of drying grain material in a vibrating dryer taking into account longitudinal movement. Results. A mathematical description of the process of drying grain material in a vibrating grain dryer has been formed, taking into account the longitudinal movement of grain in the form of a diffusion mathematical model for determining the distribution of moisture content and material temperature along the length of the vibration transporting support surface. Two-step identification of model parameters based on experimental data is applied. Conclusions. A mathematical description of the nonstationary drying process of grain material in a vibrating dryer taking into account longitudinal displacement is formulated, in the form of two linear inhomogeneous differential equations that can be used to optimize the regime parameters of existing grain dryers. Keywords: drying, grain material, mathematical model, diffusion displacement, moisture content, temperature, speed.

Models and methods for hybrid system identification: a systematic survey*

Dynamical systems and processes that either exhibit non-smooth behaviours (e.g. through logic control or natural phenomena) or work in different modes of operation are usually represented using hybrid systems models, i.e. mathematical models that combine continuous dynamics with discrete-event dynamics. Identification of a hybrid system includes finding switching patterns and identification of model parameters to obtain a data-driven model. This survey paper provides a systematic review of models (how to parameterize the system) and methods (how to identify unknown parameters) proposed for hybrid system identification with an exposition of recent advances and developments, and further research directions.

Joint Estimation of State-of-Health and State-of-Charge for Lithium-Ion Battery Based on Electrochemical Model Optimized by Neural Network

Accurate estimation of battery state including state-of-charge (SOC) and state-of-health (SOH) plays an important role in improving the performance and lifespan of battery packs in electric vehicles. From the perspective of electrochemical mechanisms, the battery SOC and SOH strongly depends on the status of battery electrochemical reaction processes and associated variables, especially the number of lithium-ion in solid phases. In this article, a method based on a simplified battery electrochemical model, which is optimized by back propagation neural network, was proposed to estimate the battery SOC and SOH. The simplified battery electrochemical model called single particle model is employed to obtain the number of recyclable lithium-ions at different aging degrees and the lithium-ion changes of solid particles with different loaded currents. The parameters of the electrochemical model are identified by an algorithm hybridizing Coyote Optimization Algorithm and Grey Wolf Optimizer, abbreviated to Hybrid Coyote Optimization Algorithm and Grey Wolf Optimizer for its great balance in operational speed and optimization accuracy. To improving the speed and accuracy of model parameter identification, the rang of the number of recyclable lithium-ions is narrowed by battery capacity, which is estimated by the back propagation neural network. The accuracy and robustness of the proposed method are systematically evaluated, and with the mean absolute percentage errors of promising battery capacity and SOC prediction results are less than 2% and 1.8%, respectively. The operation time of the battery SOC prediction during federal urban driving schedule cycles are less than 2 s.

Parameter Identification of a PN-Guided Incoming Missile Using an Improved Multiple-Model Mechanism

An active defense against an incoming missile requires information of it, including a guidance law parameter and a first-order lateral time constant. To this end, assuming that a missile with a proportional navigation (PN) guidance law attempts to attack an aerial target with bang-bang evasive maneuvers, a parameter identification model based on the gated recurrent unit (GRU) neural network is built in this paper. The analytic identification solutions for the guidance law parameter and the first-order lateral time constant are derived. The inputs of the identification model are available kinematic information between the aircraft and the missile, while the outputs contain the regression results of missile parameters. To increase the training speed and the identification accuracy of the Model, an output processing method called improved multiplemodel mechanism (IMMM) is proposed in this paper. The effectiveness of IMMM and the performance of the established model are demonstrated through numerical simulations under various engagement scenarios.