Nonlinear Traction Control via Wheel Velocity Observer
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Solution Overview
Problem
Current traction control systems are only partially effective in reducing wheel slippage as they calculate traction control forces based on linear forces, failing to capture the nonlinear dynamics of the wheel, which limits their ability to increase traction effectively.
Innovation Solution
A nonlinear dynamic model is used to estimate wheel velocity and uncertainty, determining average and differential gains to calculate motor torque and wheel brake torque, which are then applied to improve traction and reduce slippage, incorporating a predictive controller and online solver to optimize these calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a linear system analysis is used to calculate traction control forces, then the calculation is simple, but the traction control effectiveness is insufficient
Solution Approach 1:
The patent transforms the linear system parameters into nonlinear system parameters by incorporating wheel slip ratio and its rate of change as state variables. The observer estimates these nonlinear parameters in real-time, allowing the controller to adapt to varying traction conditions and improve control effectiveness while maintaining computational feasibility through recursive estimation algorithms.
Solution Approach 2:
The patent replaces the simple linear calculation mechanism with a sophisticated observer-based estimation system that uses nonlinear dynamic models. This substitution introduces mathematical complexity but enables accurate representation of wheel-ground interaction physics, significantly improving traction control reliability through better modeling of nonlinear friction characteristics.
2Reliability
If a nonlinear dynamic model is used to capture wheel dynamics, then the traction control effectiveness is improved, but the system complexity increases
Solution Approach 1:
The observer is designed to be self-sufficient by directly estimating wheel slip ratio and its rate of change from measurable quantities (wheel speed and acceleration) without requiring additional sensors. The nonlinear dynamic model uses readily available vehicle parameters (wheel inertia, brake torque, motor torque) to compute traction forces, eliminating the need for complex measurement systems while achieving improved control effectiveness.
Solution Approach 2:
The patent introduces an observer as an intermediary component that bridges the gap between simple measurements (wheel speed) and the complex nonlinear dynamics. This observer acts as a mathematical mediator that reconstructs the hidden state variables (wheel slip, slip rate) from observable quantities, allowing the controller to access detailed wheel-ground interaction information without direct measurement of these parameters.
3Speed
If wheel slip is reduced through traction control, then longitudinal motion is improved, but yaw motion control may be affected
Solution Approach 1:
The patent separates the control problem into two independent segments: longitudinal traction control and yaw rate control. The observer independently estimates wheel slip parameters for longitudinal motion, while a separate constraint equation handles yaw rate requirements. This segmentation allows each control objective to be optimized independently, with the constraint ensuring that yaw performance is not compromised by traction control actions.
Solution Approach 2:
The patent makes the control system dynamically adaptive by using real-time estimation of wheel slip and its rate of change. The controller continuously adjusts torque distribution based on current slip conditions while respecting yaw rate constraints. This dynamic approach allows the system to optimize longitudinal traction at each instant while adapting to changing vehicle motion requirements, including yaw control needs.
Data Source
AI summary
A vehicle includes a system and method of modeling and controlling a traction of a wheel of the vehicle. The system includes an observer, a predictive controller and an online solver. The observer receives a dynamic model parameter of the wheel and determines an estimate of a wheel velocity and an uncertainty in the wheel velocity using a non-linear model of the wheel. The predictive controller determines an average gain and differential gain from the estimate of the wheel velocity and the uncertainty in the wheel velocity. The online solver calculates a motor torque and a wheel brake torque for increasing the traction of the wheel with a road based on the average gain and the differential gain. The motor torque and the wheel brake torque are applied at the vehicle.