Kinematics-based parameter-varying observer design for sideslip angle estimation

2014 ◽  
Author(s):  
Haiping Du ◽  
Weihua Li
Keyword(s):  
Observer Design ◽  
Sideslip Angle ◽  
Angle Estimation ◽  
Parameter Varying

Robust Sideslip Angle Estimation for Over-Actuated Electric Vehicles: A Linear Parameter Varying System Approach

2013 ◽  
Author(s):  
Yan Chen ◽  
Junmin Wang
Keyword(s):  
Friction Force ◽  
System Approach ◽  
Estimation Method ◽  
Observer Design ◽  
Linear Parameter ◽  
Sideslip Angle ◽  
Angle Estimation ◽  
Friction Forces ◽  
Linear Parameter Varying ◽  
Parameter Varying

A new estimation method for estimating the vehicle sideslip angle, mainly based on a linear parameter varying (LPV) model with independently estimated tire friction forces, is proposed for electric ground vehicles (EGVs) with four independent in-wheel motors. By utilizing the individual wheel dynamics, the longitudinal ground friction force is estimated from a PID observer based on a descriptor linear system approach. Moreover, the lateral ground friction force for each wheel is estimated through the friction ellipse relationship given the estimated longitudinal friction force, without relying on explicit tire models. Since the estimation errors of friction forces may bring parameter uncertainty for the LPV system, robust analysis with desired H-infinity performance is given for the observer design of the LPV modeling. This method is specially proposed for large tire slip angles and lateral friction forces. Simulation results for different maneuvers validate this novel sideslip angle estimation method.

Vehicle sideslip angle estimation using nonlinear parameter-varying observers

2015 ◽  
Author(s):  
Boulaid Boulkroune ◽  
Sebastiaan V. Aalst ◽  
Erwin Rademakers ◽  
Koen Sannen ◽  
Norddin El Ghouti
Keyword(s):  
Nonlinear Parameter ◽  
Sideslip Angle ◽  
Angle Estimation ◽  
Parameter Varying

Structured Robust Linear Parameter-Varying Vehicle Sideslip Angle Estimation

2019 ◽  
Author(s):  
Jingqiang Zha ◽  
Junmin Wang ◽  
Min Li ◽  
Xin Zhang ◽  
Xiao Yu
Keyword(s):  
Controller Design ◽  
Estimation Error ◽  
Estimation Method ◽  
Vehicle Speed ◽  
Linear Parameter ◽  
Sideslip Angle ◽  
Angle Estimation ◽  
Linear Parameter Varying ◽  
Parameter Varying ◽  
Hil Simulation

Abstract Non-smooth structured robust controller design has drawn a lot of attention recently due to its ability to deal with uncertainty and its convenience for implementation. In this paper, the method is extended to design the structured robust linear parameter-varying (LPV) estimator by pulling out scheduling variables from estimator using linear fractional transformation (LFT). The structured robust LPV estimator is then applied to vehicle sideslip angle estimation. Both the measured vehicle speed and estimated tire cornering stiffness are treated as scheduling variables to further reduce sideslip angle estimation error. The effects of estimator order and number of repetitiveness of scheduling variables are studied using a MATLAB/Simulink bicycle model. The developed approach is later verified in Hardware-in-the-Loop (HIL) simulation environment using dSPACE SCALEXIO and MicroAutoBox. A comprehensive high-fidelity dSPACE automotive simulation models (ASM) vehicle model is used for the real-time HIL simulation. Double-lane change and sine steer maneuvers have been implemented to verify the effectiveness of the structured robust LPV sideslip angle estimation method.

scholarly journals Running States Estimation of Autonomous Four-Wheel Independent Drive Electric Vehicle by Virtual Longitudinal Force Sensors

2019 ◽  
Vol 2019 ◽  
pp. 1-17
Author(s):  
Qiu Xia ◽  
Long Chen ◽  
Xing Xu ◽  
Yingfeng Cai ◽  
Haobin Jiang ◽  
...  
Keyword(s):  
Sliding Mode ◽  
Estimation Method ◽  
Longitudinal Force ◽  
Observer Design ◽  
Unknown Input ◽  
Sideslip Angle ◽  
Angle Estimation ◽  
Electric Driving ◽  
Wheel Model ◽  
Driving Wheel

Exact sideslip angle estimation is significant to the dynamics control of four-wheel independent drive electric vehicles. It is costly and difficult-to-popularize to equip vehicular sensors for real-time sideslip angle measurement; therefore, the reliable sideslip angle estimation method is investigated in this paper. The electric driving wheel model is proposed and applied to the longitudinal force estimation. Considering that electric driving wheel model is a nonlinear model with unknown input, an unknown input estimation method is proposed to facilitate the longitudinal force observer design, in which the adaptive high-order sliding mode observer is designed to achieve the state estimation, the analytic formula of longitudinal force is obtained by decoupling electric driving wheel model, and the longitudinal force estimator is designed by recurrence estimation method. With the designed virtual longitudinal force sensor, an adaptive attenuated Kalman filtering is proposed to estimate the vehicle running state, in which the time-varying attenuation factor is applied to weaken the past data to the current filter and the covariance of process noise and measurement noise can be adjusted adaptively. Finally, simulations and experiments are conducted and the effectiveness of proposed estimation method is validated.

scholarly journals Event-triggering $${H}_{\infty }$$-based observer combined with NN for simultaneous estimation of vehicle sideslip and roll angles with network-induced delays

2021 ◽  
Vol 103 (3) ◽  
pp. 2733-2752
Author(s):  
Maria Jesus L. Boada ◽  
Beatriz L. Boada ◽  
Hui Zhang
Keyword(s):  
Autonomous Vehicles ◽  
Networked Control System ◽  
Observer Design ◽  
Simultaneous Estimation ◽  
Road Accidents ◽  
Sideslip Angle ◽  
Current Series ◽  
Event Triggering ◽  
Communication Scheme ◽  
Proper Design

AbstractNowadays, vehicles are being fitted with systems that improve their maneuverability, stability, and comfort in order to reduce the number of accidents. Improving these aspects is of particular interest thanks to the incorporation of autonomous vehicles onto the roads. The knowledge of vehicle sideslip and roll angles, which are among the main causes of road accidents, is necessary for a proper design of a lateral stability and roll-over controller system. The problem is that these two variables cannot be measured directly through sensors installed in current series production vehicles due to their high costs. For this reason, their estimation is fundamental. In addition, there is a time delay in the relaying of information between the different vehicle systems, such as, sensors, actuators and controllers, among others. This paper presents the design of an $${H}_{\infty }$$ H ∞ -based observer that simultaneously estimates both the sideslip angle and the roll angle of a vehicle for a networked control system, with networked transmission delay based on an event-triggered communication scheme combined with neural networks (NN). To deal with the vehicle nonlinearities, NN and linear-parameter-varying techniques are considered alongside uncertainties in parameters. Both simulation and experimental results are carried out to prove the performance of observer design.

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