scholarly journals Experimental Comparison of The Two Most Used Vehicle Sideslip Angle Estimation Methods for Model-Based Design Approach

2021 ◽  
Vol 1888 (1) ◽  
pp. 012006
Author(s):  
Daniel Chindamo ◽  
Marco Gadola ◽  
Emanuele Bonera ◽  
Paolo Magri
Keyword(s):  
Design Approach ◽  
Estimation Methods ◽  
Experimental Comparison ◽  
Sideslip Angle ◽  
Angle Estimation ◽  
Model Based

A fusion methodology for sideslip angle estimation on the basis of kinematics-based and model-based approaches

2019 ◽  
Vol 234 (7) ◽  
pp. 1930-1943
Author(s):  
Xiaoyu Li ◽  
Nan Xu ◽  
Qin Li ◽  
Konghui Guo ◽  
Jianfeng Zhou
Keyword(s):  
Magnitude Estimation ◽  
Front Wheel ◽  
Lateral Acceleration ◽  
Area Network ◽  
Sideslip Angle ◽  
Small Magnitude ◽  
Angle Estimation ◽  
Model Based ◽  
Road Friction ◽  
Basic Approaches

This article introduces a reliable fusion methodology for vehicle sideslip angle estimation, which only needs the Controller Area Network–Bus signals of production vehicles and has good robustness to vehicle parameters, tire information, and road friction coefficient. The fusion methodology consists of two basic approaches: the kinematic-based approach and the model-based approach. The former is constructed into the extended Kalman filter for transient stage and large magnitude estimation, while the latter is designed to be an adaptive scheme for steady-state and small magnitude estimation. On this basis, combining the advantages of the two methods, a weight allocation strategy is proposed based on the front wheel steering angle and transient characteristics of lateral acceleration and yaw rate. The validity of the method is verified by simulation and experiment, and it is proved that the method can be effectively used for the sideslip angle estimation.

scholarly journals An Improved Trilinear Model-Based Angle Estimation Method for Co-Prime Planar Arrays

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4180
Author(s):  
Chenxi Guo ◽  
Xinhong Hao ◽  
Ping Li
Keyword(s):  
Estimation Method ◽  
Sparse Recovery ◽  
Estimation Methods ◽  
Reconstruction Method ◽  
Angle Estimation ◽  
Trilinear Decomposition ◽  
Model Based ◽  
Planar Arrays ◽  
Trilinear Model ◽  
Virtual Array

Angle estimation methods in two-dimensional co-prime planar arrays have been discussed mainly based on peak searching and sparse recovery. Peak searching methods suffer from heavy computational complexity and sparse recovery methods face some problems in selecting the regularization parameters. In this paper, we propose an improved trilinear model-based method for angle estimation for co-prime planar arrays in the view of trilinear decomposition, namely parallel factor analysis. Due to the principle of trilinear decomposition, our method does not require peak searching and can conduct auto-pairing easily, which can reduce the computational loads and avoid parameter selection problems. Furthermore, we exploit the virtual array concept of the whole co-prime planar array through the cross-correlation matrix obtained from the received signal data and present a matrix reconstruction method using the Khatri–Rao product to tackle the matrix rank deficiency problem in the virtual array condition. The simulation results show that our proposed method can not only achieve high estimation accuracy with low complexity compared to other similar approaches, but also utilize limited sensor number to implement the angle estimation tasks.

scholarly journals Reliable Sideslip Angle Estimation of Four-Wheel Independent Drive Electric Vehicle by Information Iteration and Fusion

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Te Chen ◽  
Long Chen ◽  
Xing Xu ◽  
Yingfeng Cai ◽  
Haobin Jiang ◽  
...  
Keyword(s):  
Electric Vehicle ◽  
Estimation Method ◽  
Longitudinal Force ◽  
Recursive Least Squares ◽  
Accurate Estimation ◽  
Dynamics Model ◽  
Sideslip Angle ◽  
Angle Estimation ◽  
Kinematics Model ◽  
Model Based

Accurate estimation of longitudinal force and sideslip angle is significant to stability control of four-wheel independent driven electric vehicle. The observer design problem for the longitudinal force and sideslip angle estimation is investigated in this work. The electric driving wheel model is introduced into the longitudinal force estimation, considering the longitudinal force is the unknown input of the system, the proportional integral observer is applied to restructure the differential equation of longitudinal force, and the extended Kalman filter is utilized to estimate the unbiased longitudinal force. Using the estimated longitudinal force, considering the unknown disturbances and uncertainties of vehicle model, the robust sideslip angle estimator is proposed based on vehicle dynamics model. Moreover, the recursive least squares algorithm with forgetting factor is applied to vehicle state estimation based on the vehicle kinematics model. In order to integrate the advantages of the dynamics-model-based observer and kinematics-model-based observer and improve adaptability of observer system in complex working conditions, a vehicle sideslip angle fusion estimation strategy is proposed. The simulations and experiments are implemented and the performance of proposed estimation method is validated.

scholarly journals The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2085
Author(s):  
Xue-Bo Jin ◽  
Ruben Jonhson Robert RobertJeremiah ◽  
Ting-Li Su ◽  
Yu-Ting Bai ◽  
Jian-Lei Kong
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Future Development ◽  
Data Driven ◽  
Estimation Methods ◽  
Multiple Model ◽  
Development Trends ◽  
Research Results ◽  
Model Driven ◽  
Model Based

State estimation is widely used in various automated systems, including IoT systems, unmanned systems, robots, etc. In traditional state estimation, measurement data are instantaneous and processed in real time. With modern systems’ development, sensors can obtain more and more signals and store them. Therefore, how to use these measurement big data to improve the performance of state estimation has become a hot research issue in this field. This paper reviews the development of state estimation and future development trends. First, we review the model-based state estimation methods, including the Kalman filter, such as the extended Kalman filter (EKF), unscented Kalman filter (UKF), cubature Kalman filter (CKF), etc. Particle filters and Gaussian mixture filters that can handle mixed Gaussian noise are discussed, too. These methods have high requirements for models, while it is not easy to obtain accurate system models in practice. The emergence of robust filters, the interacting multiple model (IMM), and adaptive filters are also mentioned here. Secondly, the current research status of data-driven state estimation methods is introduced based on network learning. Finally, the main research results for hybrid filters obtained in recent years are summarized and discussed, which combine model-based methods and data-driven methods. This paper is based on state estimation research results and provides a more detailed overview of model-driven, data-driven, and hybrid-driven approaches. The main algorithm of each method is provided so that beginners can have a clearer understanding. Additionally, it discusses the future development trends for researchers in state estimation.

From Conception to Implementation: A Model Based Design Approach

2004 ◽  
Vol 37 (22) ◽  
pp. 29-34 ◽  
Author(s):  
Giovanni Gaviani ◽  
Giacomo Gentile ◽  
Giovanni Stara ◽  
Luigi Romagnoli ◽  
Thomas Thomsen ◽  
...  
Keyword(s):  
Design Approach ◽  
Model Based

scholarly journals A Model-Based Control Design Approach for Linear Free-Piston Engines

2017 ◽  
Vol 139 (11) ◽  
Author(s):  
T. N. Kigezi ◽  
J. F. Dunne
Keyword(s):  
Control Design ◽  
Linear Quadratic Regulator ◽  
Design Approach ◽  
Linear Quadratic ◽  
Free Piston ◽  
Model Based Control ◽  
Model Based ◽  
Bottom Dead Center ◽  
Free Piston Engine ◽  
Advanced Control

A general design approach is presented for model-based control of piston position in a free-piston engine (FPE). The proposed approach controls either “bottom-dead-center” (BDC) or “top-dead-center” (TDC) position. The key advantage of the approach is that it facilitates controller parameter selection, by the way of deriving parameter combinations that yield both stable BDC and stable TDC. Driving the piston motion toward a target compression ratio is, therefore, achieved with sound engineering insight, consequently allowing repeatable engine cycles for steady power output. The adopted control design approach is based on linear control-oriented models derived from exploitation of energy conservation principles in a two-stroke engine cycle. Two controllers are developed: A proportional integral (PI) controller with an associated stability condition expressed in terms of controller parameters, and a linear quadratic regulator (LQR) to demonstrate a framework for advanced control design where needed. A detailed analysis is undertaken on two FPE case studies differing only by rebound device type, reporting simulation results for both PI and LQR control. The applicability of the proposed methodology to other common FPE configurations is examined to demonstrate its generality.

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