Control-Oriented Vehicle Attitude Estimation With Online Sensors Bias Compensation

2009 ◽  
Author(s):  
Giulio Panzani ◽  
Matteo Corno ◽  
Mara Tanelli ◽  
Sergio M. Savaresi ◽  
Andrea Fortina ◽  
...  
Keyword(s):  
Estimation Algorithm ◽  
Industrial Applications ◽  
Stability Control ◽  
Attitude Estimation ◽  
Recursive Identification ◽  
Sideslip Angle ◽  
Vehicle Stability Control ◽  
Estimation Scheme ◽  
Offset Compensation ◽  
Sideslip Estimation

In advanced vehicle stability control systems, the availability of online estimates of the vehicle attitude is essential. In four-wheeled vehicles, attitude information is deeply linked to vehicle sideslip angle and sideslip rate, since these variables are strictly related to instability phenomena, safety and handling performances. As direct measurements of such quantities cannot be performed with standard sensors equipment, the design of robust and efficient estimators is needed. In this paper, we tackle the problem by proposing a sideslip rate observer and by demonstrating how an existing sideslip estimation algorithm can be made robust and reliable by online compensation of the sensors bias via a recursive identification approach. The proposed method does not require any additional sensor, making the final solution suitable for industrial applications. Experimental results confirm the effectiveness of the sensors offset compensation and witness satisfactory results of the overall vehicle attitude estimation scheme.

scholarly journals Vehicle Sideslip Angle Estimation Based on Tire Model Adaptation

Electronics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 199 ◽  
Author(s):  
Kanwar Bharat Singh
Keyword(s):  
Experimental Tests ◽  
Estimation Algorithm ◽  
Rear Wheel ◽  
Stability Control ◽  
Operating Conditions ◽  
Successful Implementation ◽  
Tire Model ◽  
Sideslip Angle ◽  
Wheel Drive ◽  
Sideslip Estimation

Information about the vehicle sideslip angle is crucial for the successful implementation of advanced stability control systems. In production vehicles, sideslip angle is difficult to measure within the desired accuracy level because of high costs and other associated impracticalities. This paper presents a novel framework for estimation of the vehicle sideslip angle. The proposed algorithm utilizes an adaptive tire model in conjunction with a model-based observer. The proposed adaptive tire model is capable of coping with changes to the tire operating conditions. More specifically, extensions have been made to Pacejka's Magic Formula expressions for the tire cornering stiffness and peak grip level. These model extensions account for variations in the tire inflation pressure, load, tread depth and temperature. The vehicle sideslip estimation algorithm is evaluated through experimental tests done on a rear wheel drive (RWD) vehicle. Detailed experimental results show that the developed system can reliably estimate the vehicle sideslip angle during both steady state and transient maneuvers.

scholarly journals Nonsmooth Recursive Identification of Sandwich Systems with Backlash-Like Hysteresis

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Ruili Dong ◽  
Yonghong Tan ◽  
Hui Chen ◽  
Yangqiu Xie
Keyword(s):  
Estimation Algorithm ◽  
Dynamic Parameter ◽  
Bundle Method ◽  
Recursive Identification ◽  
Identification Algorithm ◽  
Nonsmooth Problem ◽  
Positioning Stage ◽  
Dynamic Parameter Estimation ◽  
Estimation Scheme ◽  
Sandwich System

A recursive gradient identification algorithm based on the bundle method for sandwich systems with backlash-like hysteresis is presented in this paper. In this method, a dynamic parameter estimation scheme based on a subgradient is developed to handle the nonsmooth problem caused by the backlash embedded in the system. The search direction of the algorithm is estimated based on the so-called bundle method. Then, the convergence of the algorithm is discussed. After that, simulation results on a nonsmooth sandwich system are presented to validate the proposed estimation algorithm. Finally, the application of the proposed method to anX-Ymoving positioning stage is illustrated.

Research on Vehicle Stability Based on DYC and AFS Integrated Controller

2013 ◽  
Vol 278-280 ◽  
pp. 1510-1515 ◽  
Author(s):  
Jie Tian ◽  
Ya Qin Wang ◽  
Ning Chen
Keyword(s):  
Control Method ◽  
Sliding Mode ◽  
Stability Control ◽  
Front Wheel ◽  
Vehicle Stability ◽  
Sideslip Angle ◽  
Stable Domain ◽  
Vehicle Stability Control ◽  
Integrated Controller ◽  
Yaw Moment

A new vehicle stability control method integrated direct yaw moment control (DYC) with active front wheel steering (AFS) was proposed. On the basis of the vehicle nonlinear model, vehicle stable domain was determined by the phase plane of sideslip angle and sideslip angular velocity. When the vehicle was outside the stable domain, DYC was firstly used to produce direct yaw moment, which can make vehicle inside the stable domain. Then AFS sliding mode control was used to make the sideslip angle and yaw rate track the reference vehicle model. The simulation results show that the integrated controller improves vehicle stability more effectively than using the AFS controller alone.

Robust Sideslip Angle Estimation for Lightweight Vehicles Using Smooth Variable Structure Filter

2013 ◽  
Author(s):  
Xiaoyu Huang ◽  
Junmin Wang
Keyword(s):  
Filter Design ◽  
Variable Structure ◽  
Stability Control ◽  
Vital Role ◽  
Accurate Estimation ◽  
Model Parameters ◽  
Sideslip Angle ◽  
Vehicle Stability Control ◽  
Active Research ◽  
Smooth Variable Structure Filter

In the design of vehicle stability control (VSC) systems for ground vehicles, sideslip angle plays a vital role and its estimation has long been an active research topic. Accurate estimation of sideslip angle is more difficult for lightweight vehicles (LWVs) because their parameters are prone to significant changes with loading conditions — the amount and position of the payload. In this paper, a robust sideslip angle estimator based on a recently emerging smooth variable structure filter (SVSF) is presented. This sideslip angle estimator is suitable for LWVs because it is almost non-sensitive to the changes of the system parameters. A four-state vehicle lateral dynamic model including a pseudo-Burckhardt tire model is employed in the filter design. Compared with the widely utilized extended Kalman filter (EKF), the SVSF shows much better robustness against modeling errors. It is also more favorable in terms of tuning effort and computational speed. Simulation studies were conducted based on a high-fidelity vehicle model in CarSim®, where the vehicle took the form of a lightweight electric ground vehicle with independent in-wheel motors. The performance of the SVSF was shown by comparisons against the EKF under different settings for model parameters.

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