scholarly journals ONLINE PARAMETER IDENTIFICATION OF RICE TRANSPLANTER MODEL BASED ON IPSO-EKF ALGORITHM

2020 ◽  
Vol 61 (2) ◽  
pp. 25-34 ◽  
Author(s):  
Yibo Li ◽  
Hang Li ◽  
Xiaonan Guo
Keyword(s):  
Parameter Identification ◽  
Measured Data ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Output Data ◽  
Swarm Optimization ◽  
Model Based ◽  
Simulation Results ◽  
Filtering Effect ◽  
Ipso Algorithm

In order to improve the accuracy of rice transplanter model parameters, an online parameter identification algorithm for the rice transplanter model based on improved particle swarm optimization (IPSO) algorithm and extended Kalman filter (EKF) algorithm was proposed. The dynamic model of the rice transplanter was established to determine the model parameters of the rice transplanter. Aiming at the problem that the noise matrices in EKF algorithm were difficult to select and affected the best filtering effect, the proposed algorithm used the IPSO algorithm to optimize the noise matrices of the EKF algorithm in offline state. According to the actual vehicle tests, the IPSO-EKF was used to identify the cornering stiffness of the front and rear tires online, and the identified cornering stiffness value was substituted into the model to calculate the output data and was compared with the measured data. The simulation results showed that the accuracy of parameter identification for the rice transplanter model based on the IPSO-EKF algorithm was improved, and established an accurate rice transplanter model.

An improved nonlinear innovation-based parameter identification algorithm for ship models

2021 ◽  
pp. 1-9
Author(s):  
Baigang Zhao ◽  
Xianku Zhang
Keyword(s):  
Parameter Identification ◽  
Test Data ◽  
Stochastic Gradient ◽  
Least Square ◽  
Gradient Algorithm ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Process Innovations ◽  
Stochastic Gradient Algorithm ◽  
Tangent Function

Abstract To solve the problem of identifying ship model parameters quickly and accurately with the least test data, this paper proposes a nonlinear innovation parameter identification algorithm for ship models. This is based on a nonlinear arc tangent function that can process innovations on the basis of an original stochastic gradient algorithm. A simulation was carried out on the ship Yu Peng using 26 sets of test data to compare the parameter identification capability of a least square algorithm, the original stochastic gradient algorithm and the improved stochastic gradient algorithm. The results indicate that the improved algorithm enhances the accuracy of the parameter identification by about 12% when compared with the least squares algorithm. The effectiveness of the algorithm was further verified by a simulation of the ship Yu Kun. The results confirm the algorithm's capacity to rapidly produce highly accurate parameter identification on the basis of relatively small datasets. The approach can be extended to other parameter identification systems where only a small amount of test data is available.

scholarly journals Online Parameter Identification and State of Charge Estimation of Battery Based on Multitimescale Adaptive Double Kalman Filter Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Wenxian Duan ◽  
Chuanxue Song ◽  
Yuan Chen ◽  
Feng Xiao ◽  
Silun Peng ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
Unscented Kalman Filter ◽  
Least Square Method ◽  
State Of Charge ◽  
Least Square ◽  
Accurate Estimation ◽  
Battery Life ◽  
Model Parameters ◽  
Simulation Results

An accurate state of charge (SOC) can provide effective judgment for the BMS, which is conducive for prolonging battery life and protecting the working state of the entire battery pack. In this study, the first-order RC battery model is used as the research object and two parameter identification methods based on the least square method (RLS) are analyzed and discussed in detail. The simulation results show that the model parameters identified under the Federal Urban Driving Schedule (HPPC) condition are not suitable for the Federal Urban Driving Schedule (FUDS) condition. The parameters of the model are not universal through the HPPC condition. A multitimescale prediction model is also proposed to estimate the SOC of the battery. That is, the extended Kalman filter (EKF) is adopted to update the model parameters and the adaptive unscented Kalman filter (AUKF) is used to predict the battery SOC. The experimental results at different temperatures show that the EKF-AUKF method is superior to other methods. The algorithm is simulated and verified under different initial SOC errors. In the whole FUDS operating condition, the RSME of the SOC is within 1%, and that of the voltage is within 0.01 V. It indicates that the proposed algorithm can obtain accurate estimation results and has strong robustness. Moreover, the simulation results after adding noise errors to the current and voltage values reveal that the algorithm can eliminate the sensor accuracy effect to a certain extent.

scholarly journals On the Identification of Coupled Pitch and Heave Motions Using Opposition-Based Particle Swarm Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Yuntao Dai ◽  
Liqiang Liu ◽  
Shanshan Feng
Keyword(s):  
Particle Swarm Optimization ◽  
Parameter Identification ◽  
Learning Algorithm ◽  
Particle Swarm ◽  
Identification System ◽  
Identification Algorithm ◽  
Swarm Optimization ◽  
Disturbing Force ◽  
Hydrodynamic Parameters ◽  
Opposition Based Learning

A mathematical model must be established to study the motions of ships in order to control them effectively. An assessment of the model depends on the accuracy of hydrodynamic parameters. An algorithm for the parameter identification of the coupled pitch and heave motions in ships is, thus, put forward in this paper. The algorithm proposed is based on particle swarm optimization (PSO) and the opposition-based learning theory known as opposition-based particle swarm optimization (OPSO). A definition of the opposition-based learning algorithm is given first of all, with ideas on how to improve this algorithm and its process being presented next. Secondly, the design of the parameter identification algorithm is put forward, modeling the disturbing force and disturbing moment of the identification system and the output parameters of the identification system. Then, the problem involving the hydrodynamic parameters of motions is identified and the coupled pitch and heave motions of a ship described as an optimization problem with constraints. Finally, the numerical simulations of different sea conditions with unknown parameters are carried out using the PSO and OPSO algorithms. The simulation results show that the OPSO algorithm is relatively stable in terms of the hydrodynamic parameters identification of the coupled pitch and heave motions.

Parameter Identification from Normal and Pathological Middle Ears Using a Tailored Parameter Identification Algorithm

2021 ◽  
Author(s):  
Benjamin Sackmann ◽  
Peter Eberhard ◽  
Michael Lauxmann
Keyword(s):  
Parameter Identification ◽  
Middle Ear ◽  
Measurement Data ◽  
High Sensitivity ◽  
Element Model ◽  
Great Sensitivity ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Good Representation ◽  
Adaptive Parameter

Abstract Current clinical practice is often unable to identify the causes of conductive hearing loss in the middle ear with sufficient certainty without exploratory surgery. Besides the large uncertainties due to interindividual variances, only partially understood cause-effect principles are a major reason for the hesitant use of objective methods such as wideband tympanometry in diagnosis, despite their high sensitivity to pathological changes. For a better understanding of objective metrics of the middle ear, this study presents a model that can be used to reproduce characteristic changes in metrics of the middle ear by altering local physical model parameters linked to the anatomical causes of a pathology. A finite-element model is therefore fitted with an adaptive parameter identification algorithm to results of a temporal bone study with stepwise and systematically prepared pathologies. The fitted model is able to reproduce well the measured quantities reflectance, impedance, umbo and stapes transfer function for normal ears and ears with otosclerosis, malleus fixation and disarticulation. In addition to a good representation of the characteristic influences of the pathologies in the measured quantities, a clear assignment of identified model parameters and pathologies consistent with previous studies is achieved. The identification results highlight the importance of the local stiffness and damping values in the middle ear for correct mapping of pathological characteristics, and address the challenges of limited measurement data and wide parameter ranges from literature. The great sensitivity of the model with respect to pathologies indicates a high potential for application in model-based diagnosis.

Comparison of measured and simulated distribution of microbial biomass in subsurface vertical flow constructed wetlands

2007 ◽  
Vol 56 (3) ◽  
pp. 233-240 ◽  
Author(s):  
G. Langergraber ◽  
A. Tietz ◽  
R. Haberl
Keyword(s):  
Microbial Biomass ◽  
Constructed Wetlands ◽  
Reactive Transport ◽  
Municipal Wastewater ◽  
Subsurface Flow ◽  
Measured Data ◽  
Model Parameters ◽  
Nitrogen And Phosphorus ◽  
Simulation Results ◽  
Subsurface Flow Constructed Wetlands

The multi-component reactive transport module CW2D has been developed to model transport and reactions of the main constituents of municipal wastewater in subsurface flow constructed wetlands and is able to describe the biochemical elimination and transformation processes for organic matter, nitrogen and phosphorus. It has been shown that simulation results match the measured data when the flow model can be calibrated well. However, there is a need to develop experimental techniques for the measurement of CW2D model parameters to increase the quality of the simulation results. Over the last years methods to characterise the microbial biocoenosis in vertical subsurface flow constructed wetlands have been developed. The paper shows measured data for microbial biomass and their comparison with simulation results using different heterotrophic lysis rate constants.

Parameter identification of a differentiable Bouc-Wen model using constrained extended Kalman filter

2020 ◽  
pp. 147592172092943
Author(s):  
Dan Li ◽  
Yang Wang
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
Extended Kalman Filter ◽  
Structural Safety ◽  
Model Parameters ◽  
Suitable Model ◽  
Identification Algorithm ◽  
Hysteretic Model ◽  
System Equations ◽  
Parameter Constraints

Hysteresis is of critical importance to structural safety under severe dynamic loading conditions. One of the widely used hysteretic models for civil structures is the Bouc-Wen model, the effectiveness of which depends on suitable model parameters. The locally non-differentiable governing equation of the conventional Bouc-Wen model poses difficulty on existing identification algorithms, especially the extended Kalman filter, which relies on linearized system equations to propagate state estimates and covariance. In addition, the standard extended Kalman filter usually does not incorporate parameter constraints, and therefore may result in unreasonable estimates. In this article, a modified and differentiable Bouc-Wen model, together with a constrained extended Kalman filter (CEKF), is proposed to identify the hysteretic model parameters in a reliable way. The partial derivatives of the differentiable Bouc-Wen model with respect to hysteretic parameters can be easily calculated for implementing the identification algorithm. Constrained extended Kalman filter restricts the Kalman gain to ensure that the estimates of parameters satisfy constraints from physical laws. Parameter identification using simulated and experimental data collected from a four-story structure demonstrates that constrained extended Kalman filter can achieve more reliable identification results than the standard extended Kalman filter.

scholarly journals Online Parameter Identification of Lithium-Ion Batteries Using a Novel Multiple Forgetting Factor Recursive Least Square Algorithm

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3180 ◽  
Author(s):  
Bizhong Xia ◽  
Rui Huang ◽  
Zizhou Lao ◽  
Ruifeng Zhang ◽  
Yongzhi Lai ◽  
...  
Keyword(s):  
Parameter Identification ◽  
Lithium Ion ◽  
Least Square ◽  
Estimation Methods ◽  
Accurate Estimation ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Recursive Least Square ◽  
Forgetting Factor ◽  
Temperature State

The model parameters of the lithium-ion battery are of great importance to model-based battery state estimation methods. The fact that parameters change in different rates with operation temperature, state of charge (SOC), state of health (SOH) and other factors calls for an online parameter identification algorithm that can track different dynamic characters of the parameters. In this paper, a novel multiple forgetting factor recursive least square (MFFRLS) algorithm was proposed. Forgetting factors were assigned to each parameter, allowing the algorithm to capture the different dynamics of the parameters. Particle swarm optimization (PSO) was utilized to determine the optimal forgetting factors. A state of the art SOC estimator, known as the unscented Kalman filter (UKF), was combined with the online parameter identification to create an accurate estimation of SOC. The effectiveness of the proposed method was verified through a driving cycle under constant temperature and three different driving cycles under varied temperature. The single forgetting factor recursive least square (SFFRLS)-UKF and UKF with fixed parameter were also tested for comparison. The proposed MFFRLS-UKF method obtained an accurate estimation of SOC especially when the battery was running in an environment of changing temperature.

Real-Time Battery Model Identification Using a Two Time-Scaled Approach

2013 ◽  
Author(s):  
Yiran Hu ◽  
Yue-Yun Wang
Keyword(s):  
Parameter Identification ◽  
Least Squares ◽  
State Estimation ◽  
Real Time ◽  
Recursive Least Squares ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Model Parameter ◽  
Battery Model ◽  
Model Parameter Identification

Battery state estimation (BSE) is one of the most important design aspects of an electrified propulsion system. It includes important functions such as state-of-charge estimation which is essentially for the energy management system. A successful and practical approach to battery state estimation is via real time battery model parameter identification. In this approach, a low-order control-oriented model is used to approximate the battery dynamics. Then a recursive least squares is used to identify the model parameters in real time. Despite its good properties, this approach can fail to identify the optimal model parameters if the underlying system contains time constants that are very far apart in terms of time-scale. Unfortunately this is the case for typical lithium-ion batteries especially at lower temperatures. In this paper, a modified battery model parameter identification method is proposed where the slower and faster battery dynamics are identified separately. The battery impedance information is used to guide how to separate the slower and faster dynamics, though not used specifically in the identification algorithm. This modified algorithm is still based on least squares and can be implemented in real time using recursive least squares. Laboratory data is used to demonstrate the validity of this method.

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