Estimation of Sideslip Angle with Tire-Road Friction Adaptation Using Nonlinear Observability Theory

2022 ◽  
pp. 627-653
Author(s):  
Fan Xu ◽  
Hui Chen ◽  
Xiang Wang ◽  
Junxi Xiong
Keyword(s):  
Sideslip Angle ◽  
Road Friction ◽  
Nonlinear Observability

An extended Luenberger observer for estimation of vehicle sideslip angle and road friction

2014 ◽  
Vol 66 (4) ◽  
pp. 385 ◽  
Author(s):  
Nenggen Ding ◽  
Wen Chen ◽  
Yipeng Zhang ◽  
Guoyan Xu ◽  
Feng Gao
Keyword(s):  
Sideslip Angle ◽  
Luenberger Observer ◽  
Road Friction

scholarly journals Application of Recursive Least Square Algorithm on Estimation of Vehicle Sideslip Angle and Road Friction

2010 ◽  
Vol 2010 ◽  
pp. 1-18 ◽  
Author(s):  
Nenggen Ding ◽  
Saied Taheri
Keyword(s):  
Least Square ◽  
Recursive Least Square ◽  
Control Logic ◽  
Sideslip Angle ◽  
Active Steering ◽  
Direct Yaw Moment Control ◽  
Road Friction ◽  
Yaw Moment Control ◽  
Moment Control ◽  
Yaw Moment

A recursive least square (RLS) algorithm for estimation of vehicle sideslip angle and road friction coefficient is proposed. The algorithm uses the information from sensors onboard vehicle and control inputs from the control logic and is intended to provide the essential information for active safety systems such as active steering, direct yaw moment control, or their combination. Based on a simple two-degree-of-freedom (DOF) vehicle model, the algorithm minimizes the squared errors between estimated lateral acceleration and yaw acceleration of the vehicle and their measured values. The algorithm also utilizes available control inputs such as active steering angle and wheel brake torques. The proposed algorithm is evaluated using an 8-DOF full vehicle simulation model including all essential nonlinearities and an integrated active front steering and direct yaw moment control on dry and slippery roads.

Sliding Surface Envelope Control: Keeping the Vehicle Within a Safe State-Space Boundary

2010 ◽  
Author(s):  
Carrie G. Bobier ◽  
Shinichiro Joe ◽  
J. Christian Gerdes
Keyword(s):  
State Space ◽  
Stability Control ◽  
Control Input ◽  
Sliding Surface ◽  
Sideslip Angle ◽  
Road Friction ◽  
Surface Envelope ◽  
Safe Region ◽  
Space Boundary ◽  
State Growth

Stability control systems on the market today, while effective, operate without full information on the vehicle states and road friction properties. This paper presents a vehicle control scheme that takes into account vehicle state information on sideslip angle and yaw rate, as well as road coefficient of friction, to keep the vehicle within a safe region of the state space. The controller limits state growth outside of the safe area to a sliding surface defined by the distance to the closest operating point in the safe region. Experimental results validate a simple version of the controller on a low friction surface. The controller successfully stabilizes the vehicle using steer-by-wire as a control input.

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.

Vehicle Sideslip Angle Estimation Through Neural Networks: Application to Numerical Data

2006 ◽  
Author(s):  
Stefano Melzi ◽  
Ferruccio Resta ◽  
Edoardo Sabbioni
Keyword(s):  
Neural Networks ◽  
Vehicle Dynamics ◽  
Network Performance ◽  
Numerical Data ◽  
Sideslip Angle ◽  
Network Training ◽  
The Neural Network ◽  
Road Friction ◽  
Complete Series ◽  
Real Vehicle

Aim of this paper is to evaluate the possibility of estimating the vehicle sideslip angle through a non-structured algorithm based on neural networks. Results reported are relevant to a numerical investigation of the network performance which can be regarded as preliminary stage for the application on a real vehicle. A numerical model is used to describe the vehicle dynamics and to generate the inputs for the neural network; with an appropriate set of manoeuvres for network training the non-structured algorithm provides reliable results when applied to a complete series of handling manoeuvres carried out with different tire-road friction coefficients.

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