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

Author(s):  
Yan Chen ◽  
Junmin Wang

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.

Author(s):  
Jingqiang Zha ◽  
Junmin Wang ◽  
Min Li ◽  
Xin Zhang ◽  
Xiao Yu

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.


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