Composite Path Tracking Control for Tractor–Trailer Vehicles Via Constrained Model Predictive Control and Direct Adaptive Fuzzy Techniques

Author(s):  
Ming Yue ◽  
Xiaoqiang Hou ◽  
Wenbin Hou

Tractor–trailer vehicles will suffer from nonholonomic constraint, uncertain disturbance, and various physical limits, when they perform path tracking maneuver autonomously. This paper presents a composite path tracking control strategy to tackle the various problems arising from not only vehicle kinematic but also dynamic levels via two powerful control techniques. The proposed composite control structure consists of a model predictive control (MPC)-based posture controller and a direct adaptive fuzzy-based dynamic controller, respectively. The former posture controller can make the underactuated trailer midpoint follow an arbitrary reference trajectory given by the earth-fixed frame, as well as satisfying various physical limits. Meanwhile, the latter dynamic controller enables the vehicle velocities to track the desired velocities produced by the former one, and the global asymptotical convergence of dynamic controller is strictly guaranteed in the sense of Lyapunov stability theorem. The simulation results illustrate that the presented control strategy can achieve a coordinated control effect for the sophisticated tractor–trailer vehicles, thereby enhancing their movement performance in complex environments.

Author(s):  
Irfan Khan ◽  
Stefano Feraco ◽  
Angelo Bonfitto ◽  
Nicola Amati

Abstract This paper presents a controller dedicated to the lateral and longitudinal vehicle dynamics control for autonomous driving. The proposed strategy exploits a Model Predictive Control strategy to perform lateral guidance and speed regulation. To this end, the algorithm controls the steering angle and the throttle and brake pedals for minimizing the vehicle’s lateral deviation and relative yaw angle with respect to the reference trajectory, while the vehicle speed is controlled to drive at the maximum acceptable longitudinal speed considering the adherence and legal speed limits. The technique exploits data computed by a simulated camera mounted on the top of the vehicle while moving in different driving scenarios. The longitudinal control strategy is based on a reference speed generator, which computes the maximum speed considering the road geometry and lateral motion of the vehicle at the same time. The proposed controller is tested in highway, interurban and urban driving scenarios to check the performance of the proposed method in different driving environments.


2020 ◽  
Vol 103 (3) ◽  
pp. 003685042095013
Author(s):  
Chunjiang Bao ◽  
Jiwei Feng ◽  
Jian Wu ◽  
Shifu Liu ◽  
Guangfei Xu ◽  
...  

The current path tracking control method is usually based on the steering wheel angle loop, which often makes the driver lose control of the automatic driving control loop. In order to involve the driver in the automatic driving control loop, and to solve the vehicle path tracking control problem with system robustness and model uncertainty, this paper puts forward a steering torque control method based on model predictive control algorithm. Based on the vehicle model, this method introduces the steering system model and the steering resistance torque model, and calculates the optimal control torque of the vehicle through the real-time vehicle status, so as to make up for the model mismatch, interference and other uncertainties, and ensure the real-time participation of the driver in the automatic driving control loop. To combine the nonlinear vehicle dynamics model with the steering column model, and to take the vehicle state parameters as the feedback variables of the model predictive controller model, then input the solution of the steering superposition control rate into the vehicle model, the design of the steering controller is realized. Finally, to carry out the simulation of lane keeping based on CarSim software and Simulink control model, and the hardware in-the-loop test on the hardware in-the-loop experimental platform of CarSim/LabVIEW-RT. The simulation and test results indicate that the designed torque loop path tracking control method based on model predictive control can help the driver track the target path better.


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