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

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.