Rear-Wheel Steering State Estimation Using LPV Yaw Rate Correction
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Solution Overview
Problem
Existing rear wheel steering systems face instability due to non-optimized tuning and external disturbances, and methods for estimating lateral slip angle and velocity using vehicle state feedback control are costly and computationally demanding, often failing to accurately simulate real-time changes.
Innovation Solution
A vehicle state variable estimator using internal CAN signals and a linear parameter varying technique to estimate lateral velocity and slip angle without additional sensors, employing Kalman filters and interpolation to correct yaw rate differences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If additional sensors are added to measure lateral slip angle and lateral velocity, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent introduces a state observer as an intermediary system that indirectly estimates lateral slip angle and lateral velocity using existing sensors (yaw rate sensor, longitudinal velocity sensor) and vehicle dynamic models, avoiding the need for direct measurement sensors while achieving accurate state information
Solution Approach 2:
The patent replaces physical measurement sensors with a computational estimation system using Kalman filters and vehicle dynamic models to derive lateral slip angle and lateral velocity from readily available vehicle state data
2Measurement precision
If nonlinear tire models or nonlinear Kalman filters are used to compensate for model inaccuracies, then measurement precision is improved, but device complexity and computational load increase
Solution Approach 1:
The patent transforms the complex nonlinear estimation problem into a linear parameter-varying problem by changing parameters (gain values, model coefficients) based on operating conditions (vehicle velocity, steering angle), allowing accurate estimation without computationally intensive nonlinear algorithms
Solution Approach 2:
The patent implements a dynamic gain scheduler that adjusts observer gains and model parameters in real-time based on vehicle operating conditions, enabling the system to adapt to changing dynamics while maintaining computational efficiency through linear approximation methods
3Adaptability or versatility
If dynamic models with real-time changes are used to estimate lateral slip angle or lateral velocity, then adaptability is improved, but manufacturing precision and model accuracy worsen due to model inaccuracies
Solution Approach 1:
The patent pre-calculates and stores optimal gain values and model parameters for different operating conditions in lookup tables, allowing the system to quickly adapt to changing vehicle velocity and steering conditions without real-time complex calculations, thereby maintaining both adaptability and accuracy
Data Source
AI summary
The present disclosure relates to a device for estimating a vehicle state variable of a host vehicle. The device includes at least: a first sensor configured to detect a first yaw rate of the host vehicle; a second sensor configured to detect a velocity of the host vehicle; and a controller configured to estimate the vehicle state variable. The controller is further configured to: estimate a second yaw rate of the host vehicle; calculate a first gain for correcting a difference between the first yaw rate and the second yaw rate; calculate an interpolation ratio that changes with the velocity; calculate a second gain by applying the interpolation ratio to the first gain; and estimate the vehicle state variable based on the second gain.