Active Fault-Tolerant Control for Steering Actuator Bias in Autonomous Vehicles Using Adaptive Sliding Mode Observer

TL;DR

This paper introduces a method to detect and correct steering actuator faults in autonomous vehicles using a sensor-free approach, improving reliability during high-speed driving.

Contribution

An adaptive sliding mode observer enables real-time bias fault detection in steering actuators without additional sensors or hardware redundancy.

Findings

The proposed method reliably detects and reconstructs steering actuator bias faults during high-speed driving.

Path-tracking accuracy improves significantly with the proposed fault-tolerant control framework.

The approach operates without additional sensors or controller switching, making it practical for real-world autonomous vehicles.

Abstract

What are the main findings?
A real-time steering actuator bias estimation method is developed using an adaptive sliding mode observer based on lateral vehicle dynamicsSteering actuator bias faults are reliably detected and reconstructed under high-speed driving conditions without requiring additional sensors or hardware redundancy

A real-time steering actuator bias estimation method is developed using an adaptive sliding mode observer based on lateral vehicle dynamics

Steering actuator bias faults are reliably detected and reconstructed under high-speed driving conditions without requiring additional sensors or hardware redundancy

What are the implications of the main findings?
The proposed approach improves the reliability of steering actuator systems by enabling continuous monitoring and diagnosis of bias-type faults.The method supports fault-tolerant autonomous driving by providing accurate fault information that can be directly utilized for control compensation.

The proposed approach improves the reliability of steering actuator systems by enabling continuous monitoring and diagnosis of bias-type faults.

The method supports fault-tolerant autonomous driving by providing accurate fault information that can be directly utilized for control compensation.

Autonomous vehicle path-tracking and lateral stability depend critically on reliable steering actuator operation. However, steering systems are susceptible to bias faults from mechanical misalignment, friction, drivetrain asymmetry, and degradation. These faults distort commanded versus actual steering inputs, causing accumulated lateral and heading errors during high-speed driving. Actuator biases manifest as constant offsets, gradual drift, or intermittent activations, which complicate reliable diagnosis. This study presents an adaptive sliding mode observer-based active fault-tolerant control framework for real-time detection, estimation, and mitigation. An extended four-state lateral error model incorporating distance and heading errors captures the influence of steering bias on vehicle behavior and stability. Adaptive observer gain tuning addresses modeling uncertainties arising from speed variations, linearization residuals, and tire stiffness changes to ensure robust estimation under realistic driving conditions. The effectiveness of the proposed method is validated through high-speed double lane change simulations considering three representative bias scenarios: an initial constant bias, a gradually increasing drift bias, and an intermittent bias. Results demonstrate reliable bias estimation and significantly improved path-tracking accuracy compared to uncompensated cases. Operating without additional sensors, hardware redundancies, or controller switching, the framework is suitable for practical implementation in autonomous vehicle steering systems.

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