A Novel Parameter Identification Method for Tire Models

2016 ◽  
Author(s):  
Junzhao Jiang ◽  
Jianwei Lu ◽  
Jinhui Li ◽  
Lei Li
Keyword(s):  
Parameter Identification ◽  
Engineering Application ◽  
Lateral Force ◽  
Longitudinal Force ◽  
Modeling And Analysis ◽  
Identification Process ◽  
Identification Method ◽  
Identification Approach ◽  
Tire Modeling ◽  
Dynamics Models

A novel parameter identification method for dynamics models of pneumatic tire was presented. The parameter identification process and methodology relate to Nelder-Mead algorithm, genetic algorithm and random value method. Based on experimental data and tire modeling of PAC2002, the lateral force parameters, longitudinal force parameters and aligning moment parameters were identified by using the presented identification approach. It can be found that the novel parameter identification approach is more accurate and efficient, which can provide theoretic evidence for tire modeling and analysis. Moreover, a software system that integrates the parameter identification methodology was compiled and developed by using MATLAB and C++, which can provide a quick and efficient parameter identification process for engineering application in the future.

scholarly journals A Nonlinear Model and Parameter Identification Method for Rubber Isolators under Shock Excitation in Underwater Vehicles

2021 ◽  
Vol 9 (11) ◽  
pp. 1282
Author(s):  
Heye Xiao ◽  
Chizhen Xu ◽  
Ruobing Wang ◽  
Peixun Yu ◽  
Jie Zhou ◽  
...  
Keyword(s):  
Parameter Identification ◽  
Nonlinear Model ◽  
Current Model ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
Transient Responses ◽  
Isolation System ◽  
Shock Excitation ◽  
Identification Process ◽  
Identification Method

Rubber isolators are usually used to protect high-precision equipment of autonomous underwater vehicles (AUVs), avoiding damage from overlarge dynamic excitation. Considering the nonlinear properties of the rubber material, the nonlinear behavior of rubber isolators under shock exaltation is hard to be predict accurately without the available modal and accurate parameters. In view of this, the present study proposes a nonlinear model and parameter identification method of rubber isolators to present their transient responses under shock excitation. First, a nonlinear model of rubber isolators is introduced for simulating their amplitude and frequency-dependent deformation under shock excitation. A corresponding dynamic equation of the isolation system is proposed and analytically solved by the Newmark method and the Newton-arithmetic mean method. Secondly, a multilayer feed-forward neural network (MFFNN) is constructed with the current model to search the parameters, in which the differences between the estimated and tested responses are minimized. The sine-sweep and drop test are planned with MFFNN to build the parameter identification process of rubber isolators. Then, a T-shaped isolator composed of high-damping silicon rubber is selected as a sample, and its parameters were determined by the current identification process. The transient responses of the isolation system are reconstructed by the current mode with the identified parameter, which show good agreement with measured responses. The accuracy of the proposed model and parameter identification method is proved. Finally, the errors between the reconstructed responses and tested responses are analyzed, and the main mode of energy attenuation in the rubber isolator is discussed in order to provide an inside view of the current model.

scholarly journals Parameter identification method of hydraulic automatic gauge control system based on chaotic wolf pack optimization algorithm

AIP Advances ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 055302
Author(s):  
Yong Zhu ◽  
Guangpeng Li ◽  
Shengnan Tang ◽  
Wanlu Jiang ◽  
Zhijian Zheng
Keyword(s):  
Control System ◽  
Parameter Identification ◽  
Optimization Algorithm ◽  
Identification Method ◽  
Automatic Gauge Control

A novel approach for experimental identification of vehicle dynamic parameters

2020 ◽  
Vol 234 (10-11) ◽  
pp. 2634-2648
Author(s):  
Di Yao ◽  
Philipp Ulbricht ◽  
Stefan Tonutti ◽  
Kay Büttner ◽  
Prokop Günther
Keyword(s):  
Parameter Identification ◽  
Dynamic Modeling ◽  
Trajectory Optimization ◽  
Dynamic Parameters ◽  
Vehicle Dynamic ◽  
Test Rig ◽  
Vehicle Simulation ◽  
Virtual Test ◽  
Identification Approach ◽  
Measuring Machine

Pervasive applications of the vehicle simulation technology are a powerful motivation for the development of modern automobile industry. As basic parameters of road vehicle, vehicle dynamic parameters can significantly influence the ride comfort and dynamics of vehicle, and therefore have to be calculated accurately to obtain reliable vehicle simulation results. Aiming to develop a general solution, which is applicable to diverse test rigs with different mechanisms, a novel model-based parameter identification approach using optimized excitation trajectory is proposed in this paper to identify the vehicle dynamic parameters precisely and efficiently. The proposed approach is first verified against a virtual test rig using a universal mechanism. The simulation verification consists of four sections: (a) kinematic analysis, including the analysis of forward/inverse kinematic and singularity architecture; (b) dynamic modeling, in which three kinds of dynamic modeling method are used to derive the dynamic models for parameter identification; (c) trajectory optimization, which aims to search for the optimal trajectory to minimize the sensitivity of parameter identification to measurement noise; and (d) multibody simulation, by which vehicle dynamic parameters are identified based on the virtual test rig in the simulation environment. In addition to the simulation verification, the proposed parameter identification approach is applied to the real test rig (vehicle inertia measuring machine) in laboratory subsequently. Despite the mechanism difference between the virtual test rig and vehicle inertia measuring machine, this approach has shown an excellent portability. The experimental results indicate that the proposed parameter identification approach can effectively identify the vehicle dynamic parameters without a high requirement of movement accuracy.

A systematic identification approach for biaxial piezoelectric stage with coupled Duhem-type hysteresis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Qun Chen ◽  
Zong-Xiao Yang ◽  
Zhumu Fu
Keyword(s):  
Parameter Identification ◽  
High Efficiency ◽  
Full Range ◽  
Unknown Parameters ◽  
Content Type ◽  
De Algorithm ◽  
Complicated Model ◽  
Identification Approach ◽  
Cross Axis ◽  
Systematic Identification

Purpose The problem of parameter identification for biaxial piezoelectric stages is still a challenging task because of the existing hysteresis, dynamics and cross-axis coupling. This study aims to find an accurate and systematic approach to tackle this problem. Design/methodology/approach First, a dual-input and dual-output (DIDO) model with Duhem-type hysteresis is proposed to depict the dynamic behavior of the biaxial piezoelectric stage. Then, a systematic identification approach based on a modified differential evolution (DE) algorithm is proposed to identify the unknown parameters of the Duhem-type DIDO model for a biaxial piezostage. The randomness and parallelism of the modified DE algorithm guarantee its high efficiency. Findings The experimental results show that the characteristics of the biaxial piezoelectric stage can be identified with adequate accuracy based on the input–output data, and the peak-valley errors account for 2.8% of the full range in the X direction and 1.5% in the Y direction. The attained results validated the correctness and effectiveness of the presented identification method. Originality/value The classical DE algorithm has many adjustment parameters, which increases the inconvenience and difficulty of using in practice. The parameter identification of Duhem-type DIDO piezoelectric model is rarely studied in detail and its successful application based on DE algorithm on a biaxial piezostage is hitherto unexplored. To close this gap, this work proposed a modified DE-based systematic identification approach. It not only can identify this complicated model with more parameters, but also has little tuning parameters and thus is easy to use.

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