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Related Experiment Video

Updated: Dec 13, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

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Published on: October 28, 2022

Fault-Tolerant Model Predictive Control Algorithm for Path Tracking of Autonomous Vehicle.

Keke Geng¹, Nikolai Alexandrovich Chulin², Ziwei Wang¹

¹School of Mechanical Engineering, Southeast University, Nanjing 211189, China.

Sensors (Basel, Switzerland)

|August 6, 2020

Summary

This study introduces a new fault-tolerant model predictive control (FTMPC) for autonomous vehicles, enhancing driving safety. The method effectively detects and isolates sensor faults for robust path tracking control.

Keywords:

autonomous vehicle fault detection and isolation model predictive control path tracking control

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Area of Science:

Automotive Engineering
Control Systems
Robotics

Background:

Fault detection and isolation (FDI) are critical for autonomous vehicle safety, yet underutilized in path tracking control.
Existing model-based FDI methods have limitations when applied to vehicle dynamics and path tracking.
Autonomous vehicles rely on sensor fusion for accurate state estimation, making them vulnerable to sensor failures.

Purpose of the Study:

To develop a novel fault-tolerant model predictive control (FTMPC) algorithm for robust path tracking control in autonomous vehicles.
To address the challenge of sensor failures impacting the reliability of path tracking systems.
To enhance the safety and performance of autonomous vehicles through advanced fault-tolerant control strategies.

Main Methods:

Established and linearized a single-track 3-DOF vehicle dynamics model using Taylor expansion.
Designed a linear time-varying model predictive control (MPC) for lateral motion, incorporating vehicle dynamics and constraints.
Developed a sensor fusion algorithm using an improved weighted approach and a fault detection algorithm combining Kalman filtering and Chi-square detection.
Implemented a fault isolation strategy through matrix multiplication during data fusion.

Main Results:

The proposed FTMPC algorithm demonstrated effective fault detection and isolation capabilities.
Simulation results validated the robust path tracking performance of the FTMPC in the presence of sensor failures.
The method significantly improved path tracking control performance compared to standard approaches during simulated sensor malfunctions.

Conclusions:

The novel FTMPC algorithm provides a robust solution for path tracking control in autonomous vehicles, even with sensor failures.
The integrated approach of advanced sensor fusion, fault detection, and isolation enhances overall system reliability and safety.
This research contributes to the advancement of fault-tolerant control systems for safer autonomous driving.