Data-Driven Enhancements for MPC-Based Path Tracking Controller in Autonomous Vehicles
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View abstract on PubMed
Summary
This summary is machine-generated.This study enhances autonomous vehicle path tracking using data-driven methods. The new approach improves accuracy, outperforming standard controllers in challenging conditions.
Area Of Science
- Autonomous Systems
- Control Theory
- Machine Learning
Background
- Accurate control models are vital for model-based control in autonomous vehicles.
- Model inaccuracies due to simplification, parameter variation, and noise degrade path tracking precision.
Purpose Of The Study
- To introduce data-driven enhancements for a Model Predictive Control (MPC)-based path tracking controller (DD-PTC) for autonomous vehicles.
- To improve the precision of vehicle path tracking by enhancing the control model accuracy.
Main Methods
- Utilizing Kolmogorov-Arnold Networks (KANs) to estimate tire lateral forces and correct cornering stiffness for a dynamic predictive model.
- Employing Gaussian Process Regression (GPR) to capture unmodeled vehicle dynamics for a comprehensive control model.
- Implementing the enhanced model within an MPC framework for steering control.
Main Results
- The DD-PTC approach demonstrated superior performance compared to standard MPC and Linear Quadratic Regulator (LQR) strategies.
- Significant reduction in lateral distance errors was observed, particularly under challenging driving scenarios.
- Validation was performed using the Simulink-CarSim simulation platform.
Conclusions
- The proposed data-driven enhancements effectively improve the accuracy of the control model for autonomous vehicle path tracking.
- DD-PTC offers a robust solution for precise steering control, outperforming conventional methods.
- The integration of KANs and GPR provides a powerful framework for addressing model uncertainties in autonomous driving.
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