Vehicle Yaw Rate Model Calibration for Adaptive Steering Control
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Solution Overview
Problem
Existing vehicle control systems face challenges in accurately predicting and controlling yaw rates during steering, particularly in situations like tire replacement, vehicle modification, or changes in road environment, which affect the accuracy of steering control.
Innovation Solution
A method and apparatus for controlling a vehicle that involves receiving data related to steering, detecting target data satisfying predetermined conditions, and using an optimization model to obtain optimized model parameters that minimize the cumulative error between predicted and measured yaw rates. This process updates the yaw rate model to improve steering control accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a preset steering control model is used for vehicle control, then the system structure is simple and ease of operation is maintained, but manufacturing precision and reliability deteriorate due to inaccurate prediction of yaw rates when vehicle dynamics change
Solution Approach 1:
The system performs automatic calibration by utilizing actual vehicle steering data and optimization algorithms to self-adjust model parameters. The calibration process is triggered automatically when cumulative error exceeds a threshold, and the system autonomously identifies target data, optimizes parameters, and updates the yaw rate model without requiring external intervention or complex manual calibration procedures.
Solution Approach 2:
The system dynamically adjusts the parameters of the yaw rate model based on actual vehicle performance data. By changing the model parameters through optimization algorithms that minimize cumulative error between predicted and actual yaw rates, the system adapts to vehicle dynamics changes while maintaining a relatively simple overall system structure.
2Reliability
If calibration is performed manually in situations like tire replacement or vehicle modification, then manufacturing precision can be maintained, but loss of time increases due to the need for repeated calibration operations
Solution Approach 1:
The system continuously monitors the cumulative error between predicted and actual yaw rates during vehicle operation. When the error exceeds a predetermined threshold, the calibration process is automatically triggered without interrupting vehicle operation. This continuous monitoring and on-demand calibration approach eliminates the need for manual calibration scheduling and reduces calibration time by performing updates in real-time or near real-time.
Solution Approach 2:
The system implements a feedback mechanism where actual vehicle steering data is continuously fed back to the calibration module. The optimization algorithm uses this feedback to adjust model parameters, minimizing the cumulative error. This closed-loop feedback system ensures high reliability of steering control while reducing calibration time by automatically adapting to changes in vehicle dynamics.
3Measurement precision
If the yaw rate model is updated frequently to maintain prediction accuracy, then manufacturing precision is improved, but use of energy and productivity are reduced due to increased computational load
Solution Approach 1:
The system performs calibration only when necessary, i.e., when the cumulative error exceeds a predetermined threshold. Rather than continuously updating the model parameters, the system applies partial calibration actions only when the error threshold is breached. This approach maintains prediction accuracy while significantly reducing computational energy consumption by avoiding unnecessary calibration operations.
4Manufacturing precision
If target data selection criteria are made strict to ensure optimization accuracy, then manufacturing precision is improved, but productivity decreases due to reduced amount of usable calibration data
Solution Approach 1:
The system dynamically adjusts the selection of target data based on the calibration needs and available data. The target data detection module identifies data points that satisfy predetermined conditions for optimization, balancing the strictness of selection criteria with the availability of sufficient calibration data. This dynamic approach ensures precise parameter optimization while maintaining adequate productivity by utilizing all suitable data points.
Data Source
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AI summary
A method of controlling a vehicle includes: receiving pieces of data related to steering of the vehicle; detecting, among the pieces of data, target pieces of data satisfying predetermined conditions; based on the target pieces of data and an optimization model, obtaining optimized model parameters that minimize a cumulative error between a predicted yaw rate of a yaw rate model of the vehicle and a measured yaw rate of the vehicle; and updating the yaw rate model of the vehicle by using the optimized model parameters.