A Particle Filter Approach for Identifying Tyre Model Parameters From Full-Scale Experimental Tests

2015 ◽  
Author(s):  
Ruixin Bao ◽  
Francesco Braghin ◽  
Federico Cheli ◽  
Edoardo Sabbioni
Keyword(s):  
Particle Filtering ◽  
Experimental Tests ◽  
Lateral Acceleration ◽  
Design Stage ◽  
Model Parameters ◽  
Passenger Cars ◽  
Sideslip Angle ◽  
Tyre Model ◽  
Lane Changes ◽  
Filter Approach

Mathematical models simulating the handling behavior of passenger cars are extensively used at a design stage for evaluating the effects of new structural solutions or control systems. The main source of uncertainty in this type of models lies in the tyre-road interaction, due high nonlinearity. Proper estimation of tyre model parameters is thus of utter importance to obtain reliable results. A methodology aimed at identifying the Magic Formula-Tyre (MF-Tyre) model coefficients of the tyres of an axle based only on the measurements carried out on board vehicle (vehicle sideslip angle, yaw rate, lateral acceleration, speed and steer angle) during standard handling maneuvers (step-steers, double lane changes, etc.) is presented in this paper. The proposed methodology is based on Particle Filtering (PF) technique. PF may become a serious alternative to classic model-based techniques, such as Kalman filters. Results of the identification procedure were first checked through simulations. Then PF was applied to experimental data collected on an real instrumented passenger-car vehicles.

A Particle Filter Approach for Identifying Tire Model Parameters From Full-Scale Experimental Tests

2016 ◽  
Vol 139 (2) ◽  
Author(s):  
Edoardo Sabbioni ◽  
Ruixin Bao ◽  
Federico Cheli ◽  
Davide Tarsitano
Keyword(s):  
Particle Filtering ◽  
Experimental Tests ◽  
Lateral Acceleration ◽  
Design Stage ◽  
Model Parameters ◽  
Tire Model ◽  
Passenger Cars ◽  
Sideslip Angle ◽  
Lane Changes ◽  
Filter Approach

Mathematical models simulating the handling behavior of passenger cars are extensively used at a design stage for evaluating the effects of new structural solutions or control systems. The main source of uncertainty in these type of models lies in tire–road interaction, due to high nonlinearity. Proper estimation of tire model parameters is thus of utter importance to obtain reliable results. This paper presents a methodology aimed at identifying the magic formula-tire (MF-Tire) model coefficients of the tires of an axle only based on measurements carried out on board vehicle (vehicle sideslip angle, yaw rate, lateral acceleration, speed, and steer angle) during standard handling maneuvers (step-steers, double lane changes, etc.). The proposed methodology is based on particle filtering (PF) technique. PF may become a serious alternative to classic model-based techniques, such as Kalman filters. Results of the identification procedure were first checked through simulations. Then, PF was applied to experimental data collected using an instrumented passenger car.

Identification of Tire Model Parameters Through Full Vehicle Experimental Tests

2011 ◽  
Vol 133 (3) ◽  
Author(s):  
Francesco Braghin ◽  
Federico Cheli ◽  
Edoardo Sabbioni
Keyword(s):  
Laboratory Tests ◽  
Experimental Tests ◽  
Contact Forces ◽  
Lateral Acceleration ◽  
Design Stage ◽  
Model Parameters ◽  
Tire Model ◽  
Identification Procedure ◽  
Sideslip Angle ◽  
Tyre Model

Individual tire model parameters are traditionally derived from expensive component indoor laboratory tests as a result of an identification procedure minimizing the error with respect to force and slip measurements. These parameters are then transferred to vehicle models used at a design stage to simulate the vehicle handling behavior. A methodology aimed at identifying the Magic Formula-Tyre (MF-Tyre) model coefficients of each individual tire for pure cornering conditions based only on the measurements carried out on board vehicle (vehicle sideslip angle, yaw rate, lateral acceleration, speed and steer angle) during standard handling maneuvers (step-steers) is instead presented in this paper. The resulting tire model thus includes vertical load dependency and implicitly compensates for suspension geometry and compliance (i.e., scaling factors are included into the identified MF coefficients). The global number of tests (indoor and outdoor) needed for characterizing a tire for handling simulation purposes can thus be reduced. The proposed methodology is made in three subsequent steps. During the first phase, the average MF coefficients of the tires of an axle and the relaxation lengths are identified through an extended Kalman filter. Then the vertical loads and the slip angles at each tire are estimated. The results of these two steps are used as inputs to the last phase, where, the MF-Tyre model coefficients for each individual tire are identified through a constrained minimization approach. Results of the identification procedure have been compared with experimental data collected on a sport vehicle equipped with different tires for the front and the rear axles and instrumented with dynamometric hubs for tire contact forces measurement. Thus, a direct matching between the measured and the estimated contact forces could be performed, showing a successful tire model identification. As a further verification of the obtained results, the identified tire model has also been compared with laboratory tests on the same tire. A good agreement has been observed for the rear tire where suspension compliance is negligible, while front tire data are comparable only after including a suspension compliance compensation term into the identification procedure.

scholarly journals A Particle Filtering Approach for Fault Detection and Isolation of UAV IMU Sensors: Design, Implementation and Sensitivity Analysis

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3066
Author(s):  
Egidio D’Amato ◽  
Vito Antonio Nardi ◽  
Immacolata Notaro ◽  
Valerio Scordamaglia
Keyword(s):  
Sensitivity Analysis ◽  
Fault Detection ◽  
Flight Control ◽  
Particle Filtering ◽  
Low Cost ◽  
Experimental Tests ◽  
Attitude Estimation ◽  
Fault Detection And Isolation ◽  
Raspberry Pi ◽  
Filter Approach

Sensor fault detection and isolation (SFDI) is a fundamental topic in unmanned aerial vehicle (UAV) development, where attitude estimation plays a key role in flight control systems and its accuracy is crucial for UAV reliability. In commercial drones with low maximum take-off weights, typical redundant architectures, based on triplex, can represent a strong limitation in UAV payload capabilities. This paper proposes an FDI algorithm for low-cost multi-rotor drones equipped with duplex sensor architecture. Here, attitude estimation involves two 9-DoF inertial measurement units (IMUs) including 3-axis accelerometers, gyroscopes and magnetometers. The SFDI algorithm is based on a particle filter approach to promptly detect and isolate IMU faulted sensors. The algorithm has been implemented on a low-cost embedded platform based on a Raspberry Pi board. Its effectiveness and robustness were proved through experimental tests involving realistic faults on a real tri-rotor aircraft. A sensitivity analysis was carried out on the main algorithm parameters in order to find a trade-off between performance, computational burden and reliability.

scholarly journals Life Prediction Model of Mineral Admixture Cement Based-Materials under Early Age CO2-Erosion

Coatings ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 413
Author(s):  
Saisai Wang ◽  
Jian Chen ◽  
Xiaodong Wen
Keyword(s):  
Prediction Model ◽  
Life Prediction ◽  
Experimental Tests ◽  
Mineral Admixture ◽  
Model Parameters ◽  
Influence Coefficient ◽  
Test Pieces ◽  
Cement Based Materials ◽  
Particle Group ◽  
Life Prediction Model

Most of the existing models of structural life prediction in early carbonized environment are based on accelerated erosion after standard 28 days of cement-based materials, while cement-based materials in actual engineering are often exposed to air too early. These result in large predictions of the life expectancy of mineral-admixture cement-based materials under early CO2-erosion and affecting the safe use of structures. To this end, different types of mineral doped cement-based material test pieces are formed, and early CO2-erosion experimental tests are carried out. On the basis of the analysis of the existing model, the influence coefficient of CO2-erosion of the mineral admixture Km is introduced, the relevant function is given, and the life prediction model of the mineral admixture cement-based material under the early CO2-erosion is established and the model parameters are determined by using the particle group algorithm (PSO). It has good engineering applicability and guiding significance.

scholarly journals A Multi Objective Evolutionary Algorithm for the Parameters Extraction of Organic Thin Film Transistors Models

Electronics ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 939
Author(s):  
Rosario Schiano Lo Moriello ◽  
Davide Ruggiero ◽  
Leopoldo Angrisani ◽  
Enzo Caputo ◽  
Francesco de Pandi ◽  
...  
Keyword(s):  
Thin Film ◽  
Evolutionary Algorithm ◽  
Design Stage ◽  
Model Parameters ◽  
Organic Transistors ◽  
Organic Thin Film ◽  
Multi Objective ◽  
Application Fields ◽  
Channel Lengths ◽  
P Type

Thanks to their peculiar features, organic transistors are proving to be a valuable alternative to traditional semiconducting devices in several application fields; however, before releasing their exploitation, simulating their behaviour through adequate circuital models could be advisable during the design stage of electronic circuits and/or boards. Consequently, accurately extracting the parameter value of those models is fundamental to developing useful libraries for hardware design environments. To face the considered problem, the authors present a method based on successive application of Single- and Multi-Objective Evolutionary Algorithm for the optimal tuning of model parameters of organic transistors on thin film (OTFT). In particular, parameters are first roughly estimated to assure the best fit with the experimental transfer characteristics; the estimates are successively refined through the multi-objective strategy by also matching the values of the experimental mobility. The performance of the method has been assessed by estimating the parameters value of both P-type and N-type OTFTs characterized by different values of channel lengths; the obtained results evidence that the proposed method can obtain suitable parameters values for all the considered channel lengths.

Experience with the IMMa tyre test bench for the determination of tyre model parameters using genetic techniques

2005 ◽  
Vol 43 (sup1) ◽  
pp. 253-266 ◽  
Author(s):  
J. A. Cabrera ◽  
A. Ortiz ◽  
E. Carabias ◽  
A. Simón
Keyword(s):  
Test Bench ◽  
Model Parameters ◽  
Tyre Model ◽  
Genetic Techniques

scholarly journals Elasto-plastic-adhesive DEM model for simulating hillslope debris flows: cross comparison with field experiments

2018 ◽  
Author(s):  
Adel Albaba ◽  
Massimiliano Schwarz ◽  
Corinna Wendeler ◽  
Bernard Loup ◽  
Luuk Dorren
Keyword(s):  
Numerical Model ◽  
Flow Velocity ◽  
Debris Flows ◽  
Field Experiments ◽  
Initial Conditions ◽  
Experimental Tests ◽  
Height Velocity ◽  
Model Parameters ◽  
Fine Content ◽  
Adhesive Model

Abstract. This paper presents a Discrete Element-based elasto-plastic-adhesive model which is adapted and tested for producing hillslope debris flows. The numerical model produces three phases of particle contacts: elastic, plastic and adhesion. The model capabilities of simulating different types of cohesive granular flows were tested with different ranges of flow velocities and heights. The basic model parameters, being the basal friction (ϕb) and normal restitution coefficient (ϵn), were calibrated using field experiments of hillslope debris flows impacting two sensors. Simulations of 50 m3 of material were carried out on a channelized surface that is 41 m long and 8 m wide. The calibration process was based on measurements of flow height, flow velocity and the pressure applied to a sensor. Results of the numerical model matched well those of the field data in terms of pressure and flow velocity while less agreement was observed for flow height. Those discrepancies in results were due in part to the deposition of material in the field test which are not reproducible in the model. A parametric study was conducted to further investigate that effect of model parameters and inclination angle on flow height, velocity and pressure. Results of best-fit model parameters against selected experimental tests suggested that a link might exist between the model parameters ϕb and ϵn and the initial conditions of the tested granular material (bulk density and water and fine contents). The good performance of the model against the full-scale field experiments encourages further investigation by conducting lab-scale experiments with detailed variation of water and fine content to better understand their link to the model's parameters.

Effects of asphalt concrete characteristics on cohesive zone model parameters of hot mix asphalt mixtures

2016 ◽  
Vol 43 (3) ◽  
pp. 226-232 ◽  
Author(s):  
S. Pirmohammad ◽  
H. Khoramishad ◽  
M.R. Ayatollahi
Keyword(s):  
Asphalt Concrete ◽  
Cohesive Energy ◽  
Cohesive Zone ◽  
Hot Mix Asphalt ◽  
Cohesive Zone Model ◽  
Experimental Tests ◽  
Model Parameters ◽  
Zone Model ◽  
Binder Type ◽  
Air Void

In this paper, the effects of the main asphalt concrete characteristics including the binder type and the air void percentage on the cohesive zone model (CZM) parameters were studied. Experimental tests were conducted on semi-circular bend (SCB) specimens made of asphalt concrete and the fracture behavior was simulated using a proper CZM. The CZM parameters of various hot mix asphalt (HMA) mixtures were determined using the SCB experimental results. Five types of HMA mixtures were tested and modeled to consider the effects of binder type and air void percentage on the CZM parameters. The results showed that as the binder in HMA mixture softened, the cohesive energy strength increased, whereas enhancing the air void percentage led to reduction of the cohesive energy and strength values. Among the studied HMA mixtures, the highest values of CZM parameters were found for the HMA mixture containing a copolymer called styrene-butadiene-styrene.

scholarly journals Constructing Accurate Equivalent Electrical Circuit Models of Lithium Iron Phosphate and Lead–Acid Battery Cells for Solar Home System Applications

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2305 ◽  
Author(s):  
Yunhe Yu ◽  
Nishant Narayan ◽  
Victor Vega-Garita ◽  
Jelena Popovic-Gerber ◽  
Zian Qin ◽  
...  
Keyword(s):  
Lithium Iron Phosphate ◽  
Electrical Circuit ◽  
Design Stage ◽  
Model Parameters ◽  
Equivalent Electrical Circuit ◽  
Cell Level ◽  
Electrical Equivalent Circuit ◽  
Battery Model ◽  
Vrla Battery ◽  
Lead Acid

The past few years have seen strong growth of solar-based off-grid energy solutions such as Solar Home Systems (SHS) as a means to ameliorate the grave problem of energy poverty. Battery storage is an essential component of SHS. An accurate battery model can play a vital role in SHS design. Knowing the dynamic behaviour of the battery is important for the battery sizing and estimating the battery behaviour for the chosen application at the system design stage. In this paper, an accurate cell level dynamic battery model based on the electrical equivalent circuit is constructed for two battery technologies: the valve regulated lead–acid (VRLA) battery and the LiFePO 4 (LFP) battery. Series of experiments were performed to obtain the relevant model parameters. This model is built for low C-rate applications (lower than 0.5 C-rate) as expected in SHS. The model considers the non-linear relation between the state of charge ( S O C ) and open circuit voltage ( V OC ) for both technologies. Additionally, the equivalent electrical circuit model for the VRLA battery was improved by including a 2nd order RC pair. The simulated model differs from the experimentally obtained result by less than 2%. This cell level battery model can be potentially scaled to battery pack level with flexible capacity, making the dynamic battery model a useful tool in SHS design.

scholarly journals An OGI model for personalized estimation of glucose and insulin concentration in plasma

2021 ◽  
Vol 18 (6) ◽  
pp. 8499-8523
Author(s):  
Weijie Wang ◽  
◽  
Shaoping Wang ◽  
Yixuan Geng ◽  
Yajing Qiao ◽  
...  
Keyword(s):  
Kalman Filtering ◽  
Gaussian Noise ◽  
Particle Filtering ◽  
In Silico ◽  
Insulin Concentration ◽  
Model Parameters ◽  
Bayesian Filtering ◽  
Extended Kalman Filtering ◽  
External Disturbances ◽  
Non Gaussian

<abstract><p>Plasma glucose concentration (PGC) and plasma insulin concentration (PIC) are two essential metrics for diabetic regulation, but difficult to be measured directly. Often, PGC and PIC are estimated from continuous glucose monitoring and insulin delivery data. Nevertheless, the inter-individual variability and external disturbance (e.g. carbohydrate intake) bring challenges for accurate estimations. This study is to estimate PGC and PIC adaptively by identifying personalized parameters and external disturbances. An observable glucose-insulin (OGI) dynamic model is established to describe insulin absorption, glucose regulation, and glucose transport. The model parameters and disturbances can be extended to observable state variables and be identified dynamically by Bayesian filtering estimators. Two basic Gaussian noise based Bayesian filtering estimators, extended Kalman filtering (EKF) and unscented Kalman filtering (UKF), are implemented. Recognizing the prevalence of non-Gaussian noise, in this study, two new filtering estimators: particle filtering with Gaussian noise (PFG), and particle filtering with mixed non-Gaussian noise (PFM) are designed and implemented. The proposed OGI model in conjunction with the estimators is evaluated using the data from 30 in-silico subjects and 10 human participants. For in-silico subjects, the OGI with PFM estimator has the ability to estimate PIC and PGC adaptively, achieving RMSE of PIC $ 9.49\pm3.81 $ mU/L, and PGC $ 0.89\pm0.19 $ mmol/L. For human, the OGI with PFM has the promise to identify disturbances ($ 95.46\%\pm0.65\% $ accurate rate of meal identification). OGI model provides a way to fully personalize the parameters and external disturbances in real time, and has potential clinical utility for artificial pancreas.</p></abstract>

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