Traditional unmanned vehicles are generally limited to work under ordinary conditions, however the control strategy under extreme conditions still needs to be studied. This paper proposes a drift controller based on Model Predictive Control (MPC). Firstly, the saturation characteristics of the tire in the drift state are analyzed. On this basis, the drift steering mechanism of the vehicle model is analyzed, and the drift equilibrium point is obtained. Then the vehicle dynamics model is linearized at the equilibrium point, and the MPC drift controller is designed to realize the steady-state drift control of the vehicle. Finally, the effectiveness of the steady-state drift controller is verified by simulation.
Commercial vehicles face challenges such as a large steering dead-zone and difficulties in directly obtaining lateral velocity through sensors. To address these issues, a model predictive control (MPC) lane keeping algorithm is designed, taking into account the characteristics of the steering dead-zone. First, a vehicle dynamics model and state error equations are established. Then, the vehicle status is estimated using an extended Kalman filter (EKF). Based on the estimated vehicle parameters, an MPC lane keeping controller is designed. Finally, depending on the different operating states of the vehicle, the compensation strategies are designed based on the yaw rate error and the dead-zone length. The lane keeping algorithm was validated through simulation experiments. The lateral velocity estimation error was less than 4.8%, and the influence of the steering dead-zone decreased by 8 degrees. The results indicate that the improved MPC lane keeping algorithm exhibits excellent tracking accuracy. The dangerous S-shaped trajectory of the vehicle is effectively avoided, and the stability of vehicle operation is improved.
Aiming at the RRT algorithm's strong randomness, the obtained path is not the shortest, and the vehicle differential constraints are not satisfied, this paper proposes an improved RRT smart car motion planning algorithm. In a priori information environment where the map is known, a reasonable metric function that satisfies the vehicle kinematics constraints is proposed, so that the path is smoother and facilitates the optimization of the back-end trajectory. In addition, a pruning optimization method that reduces path nodes and shortens the path length is also proposed. The simulation results show that the improved RRT algorithm has a smoother path than the basic RRT algorithm, with fewer path nodes and a shorter path length. Finally, a collision-free, smooth and continuous trajectory that satisfies the vehicle movement is obtained.
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