Identification Of Model Parameters Research Articles

Impact analysis of uncertainty in thermal resistor-capacitor models on model predictive control performance

Model Predictive Control (MPC) is extensively utilized for optimal control in building systems. Despite substantial research being dedicated to exploring the impact of uncertainties in external and internal disturbances on the performance of MPC, the existing studies neglect the potential impact of uncertainties in model parameter identification on control performance. To address this gap, this study quantifies the impact of model uncertainty on MPC performance through a test case in a virtual environment. Various levels of uncertainties for parameters R and C are artificially introduced to assess the MPC performance. The causes of the impact of model uncertainty on control performance are further explored through analysis. We select a first-order RC model to modelling building thermal dynamics. MPC is employed to optimize the heat pump signal with the goal of minimizing the energy cost while maintaining thermal comfort. The simulation results demonstrate that a negative deviation in model parameter identification has a more pronounced impact on MPC performance than a positive deviation, which has a negligible effect on MPC control performance. Deviations in parameters from their true values affect both heat losses from the zone and thermal capacity, thereby influencing the estimated temperature by the RC model. Consequently, these factors, in turn, affect the system’s control decisions, leading to variations in the objective function values. This study can provide an insight into the relationship of model parameters uncertainties and MPC performance and facilitate the practical application of MPC in buildings.

A novel approach for identification of PV cell model parameters with Grey Wolf Election-Based Optimization algorithm

In the past few years, photovoltaic production has significantly increased worldwide and has become a necessary element for achieving global agreements to minimize carbon dioxide emissions. Therefore, a precise and reliable simulation of PV panels is of increasing interest to researchers. However, this task is predicated on determining the PV model parameters; thus, it continues to be a highly challenging task. To overcome this challenge, we propose a swarm-based human optimization algorithm, i.e. the Grey Wolf Election-Based Optimization algorithm. This new approach combines inventive human behavior in the election process with the powerful search ability of grey wolves. The root-mean-square deviations predicted by the Grey Wolf Election-Based Optimization algorithm for a RTC France cell are 7.7386E-04, 7.5644E-04, and 7.4965E-04 for single, double, and triple-diode models, respectively. A total of thirteen of the most cited and newest meta-heuristic algorithms were employed as competitors to adequately assess the performance of the proposed algorithm. The experimental results demonstrate that the proposed algorithm outperformed the compared algorithms and provided more efficient solutions.

Machine learning application to the identification of sheet metal constitutive model parameters

Machine learning application to the identification of sheet metal constitutive model parameters

Multi-Strategy Hybrid Dung Beetle Optimization Algorithm for Parameter Identification in Photovoltaic Systems

Abstract The dung beetle optimization algorithm (DBO) is a novel meta-heuristic algorithm inspired by the behaviors of dung beetle populations, including rolling, dancing, foraging, breeding and stealing. As so far, the DBO algorithm has demonstrated success in addressing a wide range of complex engineering optimization problems. However, like many other meta-heuristic algorithms, it is also prone to certain limitations, such as slow convergence rates and the tendency to become trapped in local optima during the later stages of optimization. To overcome these limitations, this paper proposes a multi-strategy hybrid dung beetle optimization algorithm (MSDBO), which introduces the tangent flight strategy, golden sine search strategy, adaptive t-distribution sparrow perturbation strategy, and vertical crossover mutation strategy. To comprehensively evaluate the performance of MSDBO, simulations are conducted on 59 benchmark functions from CEC2014 and CEC2017. Experimental results demonstrate that MSDBO outperforms DBO, four advanced DBO variants, and several other popular algorithms in overall performance. Furthermore, MSDBO is employed for parameter identification in photovoltaic system models, further showcasing its effectiveness and reliability in real-world engineering applications.

Aircraft System Identification Using Multi-Stage PRBS Optimal Inputs and Maximum Likelihood Estimator

A new method to discover open-loop, unstable, longitudinal aerodynamic parameters, using a ‘two-stage optimization approach’ for designing optimal inputs, and with an application on the fighter aircraft platform, has been presented. System identification of supersonic aircraft requires formulating optimal inputs due to the extremely limited maneuver time, high angles of attack, restricted flight conditions, and the demand for an enhanced computational effect. A pre-requisite of the parametric model identification is to have a priori aerodynamic parameter estimates, which were acquired using linear regression and Least Squares (LS) estimation, based upon simulated time histories of outputs from heuristic inputs, using an F-16 Flight Dynamic Model (FDM). In the ‘first stage’, discrete-time pseudo-random binary signal (PRBS) inputs were optimized using a minimization algorithm, in accordance with aircraft spectral features and aerodynamic constraints. In the ‘second stage’, an innovative concept of integrating the Fisher Informative Matrix with cost function based upon D-optimality criteria and Crest Factor has been utilized to further optimize the PRBS parameters, such as its frequency, amplitude, order, and periodicity. This unique optimum design also solves the problem of non-convexity, model over-parameterization, and misspecification; these are usually caused by the use of traditional heuristic (doublets and multistep) optimal inputs. After completing the optimal input framework, parameter estimation was performed using Maximum Likelihood Estimation. A performance comparison of four different PRBS inputs was made as part of our investigations. The model performance was validated by using statistical metrics, namely the following: residual analysis, standard errors, t statistics, fit error, and coefficient of determination (R2). Results have shown promising model predictions, with an accuracy of more than 95%, by using a Single Sequence Band-limited PRBS optimum input. This research concludes that, for the identification of the decoupled longitudinal Linear Time Invariant (LTI) aerodynamic model of supersonic aircraft, optimum PRBS shows better results than the traditional frequency sweeps, such as multi-sine, doublets, square waves, and impulse inputs. This work also provides the ability to corroborate control and stability derivatives obtained from Computational Fluid Dynamics (CFD) and wind tunnel testing. This further refines control law design, dynamic analysis, flying qualities assessments, accident investigations, and the subsequent design of an effective ground-based training simulator.

A method for calibrating robotic kinematic parameters based on a multi-error source model and an optimized measurement pose set

Purpose The purpose of this study is to improve the accuracy of industrial robot kinematic parameter identification and position accuracy by solving the problem of insufficient consideration of error sources in the kinematic parameter identification model and optimizing the selection of measurement pose set. Design/methodology/approach In this study, a kinematic calibration method for industrial robots considering multiple error sources is proposed. Based on the Modified Denavit Hartenberg (MD-H) model, a robot kinematics identification model including joint reduction ratio error, target ball installation error and coordinate system transformation error is established. Taking the optimal observability index O1 and the minimum flexible deformation as the optimization objectives, a measurement pose set optimization method based on Non-dominated Sorting Genetic Algorithm II (NSGA-II) is proposed to obtain a measurement pose set with higher identification accuracy. Findings Through experiments conducted with the Nantong Zhenkang ZK1400-6 robot as the test subject, the kinematic parameters identified by the optimized measurement pose set are more accurate than the randomly selected measurement pose set, and the positioning accuracy of the robot is improved from 2.11 to 0.31 mm, an increase of 85.3%. Originality/value This study introduces a position error model that comprehensively accounts for the error sources causing positioning inaccuracies. Building on this foundation, a novel flexible deformation index is proposed to quantify the flexible deformation in the measurement pose set, thereby reducing the impact of such deformation on the position error in the model. To the best of the authors’ knowledge, for the first time, this study presents an optimization method for the measurement pose set based on NSGA-II, using the flexible deformation index and observability index as objectives for multi-objective optimization, simultaneously optimizing the pose error and Jacobian matrix in the error model.

Optimal parameter identification of photovoltaic systems based on enhanced differential evolution optimization technique

Identifying the parameters of a solar photovoltaic (PV) model optimally, is necessary for simulation, performance assessment, and design verification. However, precise PV cell modelling is critical for design due to many critical factors, such as inherent nonlinearity, existing complexity, and a wide range of model parameters. Although different researchers have recently proposed several effective techniques for solar PV system parameter identification, it is still an interesting challenge for researchers to enhance the accuracy of the PV system modelling. With the above motivation, this article suggests a stage-specific mutation strategy for the proposed enhanced differential evolution (EDE) that adopts a better search process to arrive at optimal solutions by adaptively varying the mutation factor and crossover rate at different search stages. The optimal identification of PV systems is formulated as a single objective function. It appears in the form of the Root Mean Square Error (RMSE) between the PV model current from the experimental data and the current calculated using the identified parameters considering the parameter constraints (limits). The I-V (current-voltage) characteristics/data with identified parameters are validated with the experimental data to justify the proposed approach’s accuracy and efficacy for different cells and modules. Extensive simulation has been demonstrated considering two different PV cells (RTC France & PVM-752-GaAs) and three different PV modules (ND-R250A5, STM6 40/36 & STP6 120/36). The results obtained from the proposed EDE technique show Root Mean Square Errors (RMSE) of 7.730062e-4, 7.419648e-4, and 7.33228e-4 respectively, in parameter identification of RTC France PV cell models based on single, double, and triple diodes. Also, the RMSE involved in parameter identification of PVM-752-GaAs PV cell models based on single, double, and triple diodes are 1.59256e-4, 1.408989e-4, and 1.30181e-4, respectively. The parameters identification of ND-R250A5, STM6 40/36 and STP6 120/36 PV modules involve RMSE values of 7.697716e-3, 1.772095e-3, and 1.224258e-2, respectively. All these RMSE values obtained with proposed EDE are the least as compared to other well-accepted algorithms, thereby justifying its higher accuracy.

Identification of Tank Model Parameters Used for Evaluating Groundwater Recharge Functions of Porous Pavement

Identification of Tank Model Parameters Used for Evaluating Groundwater Recharge Functions of Porous Pavement

Multi-physics coupling model parameter identification of lithium-ion battery based on data driven method and genetic algorithm

Multi-physics coupling model parameter identification of lithium-ion battery based on data driven method and genetic algorithm

IPMSM Optimization of MTPA Control Strategy Based on Switching Model Parameter Identification

IPMSM Optimization of MTPA Control Strategy Based on Switching Model Parameter Identification

Parameter Identification of PEMFC Model Using Improved Dung Beetle Optimization Algorithm

A proton exchange membrane fuel cell (PEMFC) is a complex system with multiple inputs and outputs, nonlinearity and strong coupling, and the establishment of an accurate model is the basis for evaluating the performance of PEMFC and developing control strategies. As the majority of the current intelligent algorithms tend to become stuck in local optimum when attempting to determine the PEMFC model’s parameters, resulting in low accuracy of parameter identification and poor model generalization ability, we propose an Improved Dung Beetle Optimization (IDBO) algorithm to identify the PEMFC model’s best parameters. To evaluate the IDBO algorithm’s performance, we identify the model optimal parameters of two typical commercial stacks, BCS 500 W and NedStack PS6, and the self-developed 3 kW PEMFC system, with the minimization of the sum of squared errors between the experimental output voltages and the model output voltages as the objective function. The verification results indicate that the IDBO algorithm has better convergence performance and higher parameter identification exactitude than the DBO algorithm. The robustness and applicability of the IDBO algorithm in addressing the issue of parameter identification of the PEMFC models are verified.

Parametric Identification of Nonlinear and Linear Mathematical Models of the BLDC Motor

Parametric Identification of Nonlinear and Linear Mathematical Models of the BLDC Motor

Troubleshooting of wind turbine gearboxes

Abstract Qinghai Province is rich in wind power resources, actively building wind power generation clean energy highland, large installed capacity, and in the rapid expansion, how to achieve the safe and stable operation of the wind power base is particularly critical. Wind turbine gearboxes operate 24 hours a day under poor working conditions, and key components are prone to failure, accounting for the highest proportion of maintenance costs, so it is of great significance to research intelligent assessment methods and key technologies for the health status of wind turbine gearboxes. A dynamic model of wind-machine-network coupling in wind turbine transmission chain is established to investigate the failure mechanism and dynamic evolution of transmission chain gearboxes under variable wind load. A time-frequency energy concentration algorithm based on a synchronous extraction operator is found to explore the mapping relationship between fault sources and signs. Using a multi-source signal fusion strategy and migration learning method for order spectral analysis, a system fault characteristic parameter identification model is established to implement online monitoring and fault prediction of the wind turbine operation condition, and to improve its operation reliability.

Robust Estimation of Lithium Battery State of Charge with Random Missing Current Measurement Data

The precise estimation of the state of charge (SOC) in lithium batteries is crucial for enhancing their operational lifespan. To address the issue of reduced accuracy in SOC estimation caused by the random missing values of lithium battery current measurements, a joint estimation method which combines recursive least squares with missing input data (MIDRLS) and the unscented Kalman filter (UKF) algorithm is proposed, called the MIDRLS-UKF algorithm. Firstly, the equivalent circuit model of a Thevenin battery is formulated. Then, the current imputation model is designed to interpolate the missing data, based on which the MIDRLS algorithm is derived by solving the unbiased estimation of the gradient of the objective function, thus realizing the online high-precision identification of the circuit model parameters. Furthermore, the proposed algorithm is combined with the UKF algorithm to facilitate the online precise estimation of SOC. The simulation results indicate a marked decrease in the SOC estimation error when employing the proposed joint algorithm, as opposed to the conventional forgetting factor recursive least squares (FFRLS) algorithm combined with the UKF joint estimation algorithm, which verifies the precision and effectiveness of the proposed joint algorithm.

Parameter Identification of Solid Oxide Fuel Cell Using Elman Neural Network and Dynamic Fitness Distance Balance-Manta Ray Foraging Optimization Algorithm

An accurate solid oxide fuel cell model is a prerequisite for optimizing the operation and state estimation of subsequent cell systems. Hence, this work aimed to utilize a vigoroso algorithmic tool, i.e., Elman neural network, for data prediction to enrich cell measurement data and employ the trained network model for noise reduction of voltage–current data. Furthermore, to obtain reliable cell parameters, a novel parameter identification model based on the dynamic fitness distance balance-manta ray foraging optimization (dFDB-MRFO) algorithm is proposed. Two datasets were applied to extract the electrochemical model and simple electrochemical model parameters of the solid oxide fuel cell model. To verify adequately the superiority of this method, which is compared with another seven conventional heuristic algorithms, four performance indicators were selected as evaluation criteria. Comprehensive case studies demonstrated that through data processing, the precision and robustness of identification could be effectively heightened. In general, the model fitting data obtained via parameter identification using dFDB-MRFO have excellent fitting precision contrast with the measured voltage–current data. Notably, the fitting degree obtained by dFDB-MRFO in the simple electrochemical model reached 99.95% and 99.91% under the two datasets, respectively.

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

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

Reinforcement Learning for Efficient Identification of Soliton System Parameters Across Expansive Domains

Abstract Optical solitons in mode-locked fiber lasers and optical communication links have a wide range of applications. A crucial step in studying the transmission modes of optical solitons is to investigate the relationship between equation parameters and soliton evolution using deep learning techniques. However, current identification models have a limited parameter domain search range and are greatly influenced by initialization, often leading to divergence towards incorrect parameter values. This research harnesses reinforcement learning to revamp the iterative process of the parameter identification model. Through the development of a two-stage optimization strategy, the model is capable of conducting an accurate parameter search across arbitrary domains. The investigation encompasses a series of experiments on various standard and higher-order equations, illustrating that the innovative model overcomes the impact of initialization on the parameter search, and the identified parameters are guided towards their correct values. The enhanced model markedly improves experimental efficiency and holds significant promise for advancing the research of soliton propagation dynamics and addressing intricate scenarios.

Mitigating local minima in extracting optimal parameters for photovoltaic models: An optimizer leveraging multiple initial populations (OLMIP)

The accurate identification of parameters in photovoltaic models is of paramount importance to accurately predict the electrical behavior of photovoltaic systems and improve their performance. Nowadays, this task poses a multimodal optimization problem, aiming to find the best combination of parameters that fit the model to real data. This article presents a proposal for an Optimizer Leveraging Multiple Initial Populations (OLMIP) that aims to achieve optimal solutions while effectively avoiding undesirable local optima. By utilizing a separate evolution strategy involving four distinct initial populations, followed by the construction of an elite population, the algorithm can explore multiple regions of the search space and escape local minima. Experiments were conducted with four models: the single diode, double diode, triple diode from RTC France cell, and Photowatt-PWP201 module. The mean squared errors obtained are 9.860219E-04, 9.824849E-04, 9.824849E-04, and 2.425075 E−03, respectively. These results indicate that the algorithm achieves superior or comparable accuracy to that of six competitors. Furthermore, the statistical analysis of the results, including the Wilcoxon and Friedman tests, confirms the robustness and effectiveness of the approach used for parameter estimation in photovoltaic systems. These findings open up new prospects for the improvement and optimization of photovoltaic systems.

Joint torque estimation method of industrial robot by combining error compensation model based on LSTM and iterative weighted parameter identification

It is essential for human-robot interaction to accurate joint torque value estimation of industrial robots without force/torque (F/T) sensors. The most common method to estimate the value of robot joint torque is based on dynamic model parameter identification. However, no matter how elaborate the modeling methods are used, there are always uncertainty errors when robot’s joint rotation direction changes. In this paper, an identification framework is proposed to estimate optimal model parameters, and a deep learning network is proposed to compensate for the uncertainty errors caused by the unmodeled dynamics part. At first, the dynamic parameters are estimated during the Least Squares (LS) identification process considering the noise influence and data outliers, and the nonlinear friction model is unified into the identification framework. Secondly, based on Long-Short Term Memory (LSTM) network, an error compensation model (ECM) is proposed to establish the mapping relationship between the joint motion and the identification errors. Finally, a 6-DOF robot is used for parameter identification and ECM validation. Experimental results show that the proposed identification method is better than the LS method. Compared with the torque value estimated by the identification method, the proposed ECM can compensate for the uncertainty errors and improve the torque estimation accuracy.

A stepwise parameter identification strategy for probe-based robot on-machine measurement system calibration based on sensitivity analysis

The probe-based measurement presents an economical and efficient on-machine measurement (OMM) method. However, the low position accuracy of robot presents significant challenges for probe-based robot OMM. To improve the measurement accuracy of robot OMM system, this paper calibrates the geometric parameters of robot OMM system through a closed-loop calibration method with sphere constraint. First, the geometric error model for robot OMM system is established. Given the difficulty in model parameter identification, this paper defines a new index, the relative distance sensitivity index (RDSI), to reveal the contribution of model parameter to relative distance accuracy. Subsequently, sensitivity analysis is conducted on model parameters based on RDSI and model parameters are grouped according to the analysis results. In parameter identification, parameters are identified step by step according to the grouping results. Experiment results show that after calibration, the max distance measurement error of robot OMM system is reduced from 0.54 mm to 0.12 mm.