Robust Control with Uncertain Disturbances for Vehicle Drift Motions

Professor drivers, including racing drivers, can drive cars to achieve drift motions by taking control of the steering angle in high tire slip ratios, which provides a way to improve the driving safety of autonomous vehicles. The existing studies can be divided into two kinds based on analysis methods, and the theory-based is chosen in this study. Because the recent theory based is most applied for planar models with neglect of the rollover accident risk, the nonlinear vehicle model is established by considering longitudinal, lateral, roll, and yaw motions and rolling safety with the nonlinear tire model UniTire. The drift motion mechanism is analyzed in steady and transient states to obtain drift motion conditions, including the velocity limitation and the relationship between sideslip angle and yaw rate, and vehicle main status parameters including the velocity, side-slip angle and yaw rate in drift conditions. The state-feedback controller is designed based on robust theory and LMI (linear matrix inequation) with uncertain disturbances to realize circle motions in drift conditions. The designed controller in simulations realizes drift circle motions aiming at analyzed status target values by matching the front-wheel steering angle with saturated tire forces, which satisfies the Lyapunov stability with robustness. Robust control in drift conditions solves the problem of how to control vehicles to perform drift motions with uncertain disturbances and improves the driving safety of autonomous vehicles.

[1]  J. Christian Gerdes,et al.  Neural network vehicle models for high-performance automated driving , 2019, Science Robotics.

[2]  Yeonsik Kang,et al.  Experimental Verification of a Drift Controller for Autonomous Vehicle Tracking: a Circular Trajectory Using LQR Method , 2020 .

[3]  Timothy A. Sands Development of Deterministic Artificial Intelligence for Unmanned Underwater Vehicles (UUV) , 2020, Journal of Marine Science and Engineering.

[4]  J. Christian Gerdes,et al.  Shared Steering Control Using Safe Envelopes for Obstacle Avoidance and Vehicle Stability , 2016, IEEE Transactions on Intelligent Transportation Systems.

[5]  Vijay John,et al.  Self-scheduling robust preview controllers for path tracking and autonomous vehicles , 2017, 2017 11th Asian Control Conference (ASCC).

[6]  Tao Chou,et al.  An improvement in rollover detection of articulated vehicles using the grey system theory , 2014 .

[7]  Hong Sun,et al.  A Novel Fuzzy Observer-Based Steering Control Approach for Path Tracking in Autonomous Vehicles , 2019, IEEE Transactions on Fuzzy Systems.

[8]  Konghui Guo,et al.  UniTire: unified tire model for vehicle dynamic simulation , 2007 .

[9]  Zsolt Szalay,et al.  Implementation and experimental evaluation of a MIMO drifting controller on a test vehicle , 2020, 2020 IEEE Intelligent Vehicles Symposium (IV).

[10]  Jonathan P. How,et al.  Autonomous drifting using simulation-aided reinforcement learning , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[11]  Fredrik Bruzelius,et al.  Path control in limit handling and drifting conditions using State Dependent Riccati Equation technique , 2020, Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering.

[12]  Zhihao Zhang,et al.  MPC and PSO Based Control Methodology for Path Tracking of 4WS4WD Vehicles , 2018, Applied Sciences.

[13]  Konghui Guo,et al.  UniTire steady state model: Overview and applications , 2011, 2011 3rd International Conference on Advanced Computer Control.

[14]  Konghui Guo,et al.  UniTire: Unified Tire Model , 2016 .

[15]  Ming Liu,et al.  High-Speed Autonomous Drifting With Deep Reinforcement Learning , 2020, IEEE Robotics and Automation Letters.

[16]  Stratis Kanarachos,et al.  Teaching a vehicle to autonomously drift: A data-based approach using Neural Networks , 2018, Knowl. Based Syst..

[17]  Zsolt Szalay,et al.  MIMO Controller Design for Stabilizing Vehicle Drifting , 2019, 2019 IEEE 19th International Symposium on Computational Intelligence and Informatics and 7th IEEE International Conference on Recent Achievements in Mechatronics, Automation, Computer Sciences and Robotics (CINTI-MACRo).

[18]  Dongpu Cao,et al.  Robust Longitudinal Control of Multi-Vehicle Systems—A Distributed H-Infinity Method , 2018, IEEE Transactions on Intelligent Transportation Systems.

[19]  J. Christian Gerdes,et al.  Vehicle control synthesis using phase portraits of planar dynamics , 2018, Vehicle System Dynamics.

[20]  Jian Wu,et al.  Robust Coordination Control of AFS and ARS for Autonomous vehicle Path Tracking and Stability , 2018, 2018 IEEE International Conference on Mechatronics and Automation (ICMA).

[21]  Konghui Guo,et al.  The UniTire model: a nonlinear and non-steady-state tyre model for vehicle dynamics simulation , 2005 .

[22]  J. Christian Gerdes,et al.  A Controller Framework for Autonomous Drifting: Design, Stability, and Experimental Validation , 2014 .

[23]  Qun Wang,et al.  Control Strategies on Path Tracking for Autonomous Vehicle: State of the Art and Future Challenges , 2020, IEEE Access.

[24]  Kyongsu Yi,et al.  Design of a rollover index-based vehicle stability control scheme , 2007 .

[25]  C. Huang,et al.  Steady State Drifting Controller for Rear-Wheel Independent Driving Electric Vehicels , 2020, Journal of Physics: Conference Series.

[26]  Emilio Frazzoli,et al.  On steady-state cornering equilibria for wheeled vehicles with drift , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

[27]  Yu Meng,et al.  A New Path Tracking Method Based on Multilayer Model Predictive Control , 2019, Applied Sciences.