Vehicle Parameter Identification for Vertical Dynamics

2014 ◽  
Vol 137 (2) ◽  
Author(s):  
Yan Cui ◽  
Thomas R. Kurfess
Keyword(s):  
Experimental Data ◽  
System Identification ◽  
Parameter Identification ◽  
Shock Absorber ◽  
Vehicle Model ◽  
Identification Methods ◽  
Car Model ◽  
Nonlinear Hysteresis ◽  
Hysteretic Characteristics ◽  
Identification Techniques

In this paper, a nonlinear full car model considering the nonlinear and hysteretic characteristics of the shock absorber is developed. An approach to integrate the hybrid shock absorber model into the vehicle model using system identification techniques is then presented. To validate the approach, parameter identification of the nominal linear full car model and parameter identification of the full car model with nonlinear/hysteresis shock absorber force input are compared. The target vehicle is tested on an MTS Systems Corporation tire-coupled 4-post road simulator and the experimental data validate the system identification methods proposed in this paper.

scholarly journals Modelling of a Three-Body Hinge-Barge Wave Energy Device Using System Identification Techniques

Energies ◽  
2020 ◽  
Vol 13 (19) ◽  
pp. 5129
Author(s):  
Fernando Jaramillo-Lopez ◽  
Brian Flannery ◽  
Jimmy Murphy ◽  
John V. Ringwood
Keyword(s):  
Experimental Data ◽  
System Identification ◽  
Wave Energy ◽  
Dynamic Models ◽  
Linear Time ◽  
Renewable Energy Sources ◽  
Linear Potential ◽  
Energy Device ◽  
Three Body ◽  
Identification Techniques

In order to increase the prevalence of wave energy converters (WECs), they must provide energy at competitive prices, especially when compared with other renewable energy sources. Thus, it is imperative to develop control system technologies that are able to maximize energy extraction from waves, such that the delivered energy cost is reduced. An important part of a model-based controller is the model that it uses. System identification techniques (SITs) provide methodologies to get accurate dynamic models from input-output data. However, even though these techniques are well developed in other application areas, they are seldom used in the context of WECs. This paper proposes several strategies based on SIT to get a linear time-invariant model for a three-body hinge-barge wave energy device using experimental data. The main advantage of the model obtained with this methodology, against other methods such as linear potential theory, is that this model remains valid even for relatively large waves and WEC displacements. Other advantages of this model are its simplicity and the low computational resources that it needs. Numerical simulations are carried out to show the validation of the obtained model against recorded experimental data.

Realization and Comparison of System Identification Based on Different Least Squares Methods

2012 ◽  
Vol 226-228 ◽  
pp. 2167-2170
Author(s):  
Xu Dong Zhang ◽  
Ji Fu Guan ◽  
Liang Gu
Keyword(s):  
System Identification ◽  
Transfer Function ◽  
Parameter Identification ◽  
Least Squares ◽  
Recursive Least Squares ◽  
System Model ◽  
Vibration System ◽  
Vehicle Model ◽  
Identification Theory ◽  
Discrete Transfer Function

System identification, which includes parameter identification and non-parameter identification, is to estimate its mathematical model based on the input and output observation in system. This paper discusses the system identification theory and establishes a transfer function of 1/4 vehicle’s second-order vibration system model. Through the discrete transfer function, the system’s difference equation can be obtained to identify the system in two ways, RLS (recursive least squares) and RELS (extended recursive least squares). Finally, the paper makes a comparative analysis about RLS and RELS in connection with the vehicle model. The results show that RELS method is more accurate and has stronger convergence than RLS method, which provides the basis for the researching of control system’s algorithm, simulation and making control strategy.

scholarly journals Parameter Identification of Inverter-Fed Induction Motors: A Review

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2194 ◽  
Author(s):  
Jing Tang ◽  
Yongheng Yang ◽  
Frede Blaabjerg ◽  
Jie Chen ◽  
Lijun Diao ◽  
...  
Keyword(s):  
Parameter Identification ◽  
Induction Motor ◽  
High Performance ◽  
Least Square ◽  
Recursive Least Square ◽  
Identification Methods ◽  
Voltage Signal ◽  
High Frequency Ac ◽  
Identification Techniques ◽  
Model Reference Adaptive

Induction motor parameters are essential for high-performance control. However, motor parameters vary because of winding temperature rise, skin effect, and flux saturation. Mismatched parameters will consequently lead to motor performance degradation. To provide accurate motor parameters, in this paper, a comprehensive review of offline and online identification methods is presented. In the implementation of offline identification, either a DC voltage or single-phase AC voltage signal is injected to keep the induction motor standstill, and the corresponding identification algorithms are discussed in the paper. Moreover, the online parameter identification methods are illustrated, including the recursive least square, model reference adaptive system, DC and high-frequency AC voltage injection, and observer-based techniques, etc. Simulations on selected identification techniques applied to an example induction motor are presented to demonstrate their performance and exemplify the parameter identification methods.

System Identification Methods for Improving Flutter Flight Test Techniques

2004 ◽  
Author(s):  
David Klyde ◽  
Chuck Harris ◽  
Peter M. Thompson ◽  
Edward N. Bachelder
Keyword(s):  
System Identification ◽  
Flight Test ◽  
Identification Methods

scholarly journals Comparative Analysis of Identification Methods for Mechanical Dynamics of Large-Scale Wind Turbine

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3429 ◽  
Author(s):  
Chu ◽  
Yuan ◽  
Hu ◽  
Pan ◽  
Pan
Keyword(s):  
Mutual Information ◽  
Parameter Identification ◽  
Wind Turbines ◽  
Closed Loop ◽  
Control Design ◽  
Identification Accuracy ◽  
Mass Model ◽  
Identification Methods ◽  
Advanced Control ◽  
Mechanical Dynamics

With increasing size and flexibility of modern grid-connected wind turbines, advanced control algorithms are urgently needed, especially for multi-degree-of-freedom control of blade pitches and sizable rotor. However, complex dynamics of wind turbines are difficult to be modeled in a simplified state-space form for advanced control design considering stability. In this paper, grey-box parameter identification of critical mechanical models is systematically studied without excitation experiment, and applicabilities of different methods are compared from views of control design. Firstly, through mechanism analysis, the Hammerstein structure is adopted for mechanical-side modeling of wind turbines. Under closed-loop control across the whole wind speed range, structural identifiability of the drive-train model is analyzed in qualitation. Then, mutual information calculation among identified variables is used to quantitatively reveal the relationship between identification accuracy and variables’ relevance. Then, the methods such as subspace identification, recursive least square identification and optimal identification are compared for a two-mass model and tower model. At last, through the high-fidelity simulation demo of a 2 MW wind turbine in the GH Bladed software, multivariable datasets are produced for studying. The results show that the Hammerstein structure is effective for simplify the modeling process where closed-loop identification of a two-mass model without excitation experiment is feasible. Meanwhile, it is found that variables’ relevance has obvious influence on identification accuracy where mutual information is a good indicator. Higher mutual information often yields better accuracy. Additionally, three identification methods have diverse performance levels, showing their application potentials for different control design algorithms. In contrast, grey-box optimal parameter identification is the most promising for advanced control design considering stability, although its simplified representation of complex mechanical dynamics needs additional dynamic compensation which will be studied in future.

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