Abstract
Achieving robust path tracking is essential for efficiently operating autonomous driving systems, particularly in unpredictable environments. This paper introduces a novel path-tracking control methodology utilizing a variable second-order Sliding Mode Control (SMC) approach. The proposed control strategy addresses the challenges posed by uncertainties and disturbances by reconfiguring and expanding the state-space matrix of a kinematic bicycle model guaranteeing Lyapunov stability and convergence of the system. A state prediction is integrated into the developed SMC to mitigate response time delays. Furthermore, the controller integrates adaptive mechanisms to adjust time-varying parameters within the control formulation based on longitudinal velocity, thereby enhancing path-tracking performance and reducing chattering phenomena. The effectiveness of the proposed approach is comprehensively evaluated through simulations and experiments encompassing challenging driving scenarios characterized by high-curvature paths, varying altitudes, and sensor disturbances, typical in rural driving environments. Results demonstrate that disturbances have varying impacts depending on the type of sensor affected. Real-world tests validate these findings, offering practical insights for automated vehicle path-tracking implementation.
Original language | English |
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Pages (from-to) | 1314-1325 |
Number of pages | 12 |
Journal | IEEE Open Journal of Vehicular Technology |
Volume | 5 |
DOIs | |
Publication status | Published - 2024 |
Keywords
- Automated Vehicles
- Path Tracking
- Robustness
- Rural Environments
- Sliding Mode Control
- Vehicle Control