Trajectory Planning And Motion Control For Extreme Maneuvers Of Autonomous Vehicles

Current chassis control systems try to prevent tires from locking or slipping to maintain vehicle stability.In racing instead,expert drivers intentionally perform drifting maneuvers by making tire forces saturating to reduce lap time or avoid obstacles.Current chassis control systems are subject to the limited sensoring ability of traditional vehicles and could only make use of ego vehicle information.By studying vehicle dynamics and control maneuvers in extreme conditions,we can help design advanced chassis control systems with an extended operation envelope.Based on the vehicle dynamics in different extreme scenarios,we propose linear quadratic equilibrium controller for sustained drift,trajectory planning and motion control algorithms to plan and track a reference drift trajectory for transient drift,and vehicle extreme handling maneuvers based on reinforcement learning.Firstly,we design an equilibrium controller with considering limited control authority and model uncertainty.A single-track bicycle model and a nonlinear tire model with considering friction circle limit are proposed.We analyze the vehicle dynamics under sustained drift and design a linear quadratic sustained drift controller with a mixed feedforward and feedback control structure.To deal with the limited control authority caused by tire nearly saturating and model uncertainty caused by friction coefficient's variation,we design the maximum attraction region and minimum invariant set.Secondly,a complete trajectory planning and motion control framework is proposed,which is suitable for combined extreme and non-extreme scenario.The trajectory planner divides the path horizon into different types of regions.The ruled-based sampling method is applied to find a path in extreme region,and the Rapidly-exploring Random Tree with single-track model is applied to find a path in non-extreme region.A mixed open-loop and closed-loop control technique based on the bicycle model with linear tire model is applied to track the drift trajectory.The switch policy is based on the model predict method.Next,a reinforcement learning algorithm with prior knowledge is proposed to achieve transient drift under extreme conditions.Based on Actor-Critic reinforcement learning architecture,an actor neutral network is designed to reflect the control policy and a critic neutral network is designed to evaluate the control policy.The actor network is updated through policy gradient method and the critic network is updated through Temporal-Difference method.By learning from the demonstrations and optimal control policy,the prior knowledge is applied to help actor network and critic network converge to the global optimum.The resultant control strategy is consistent with the expert drivers' behaviors performed in racing.Finally,a 1:10 real vehicle platform is constructed to verify the proposed methods.The experimental results show the proposed trajectory planning and motion control algorithm are applicable in practice.