Robust Hierarchical Model Predictive Control for Trajectory Tracking with Obstacle Avoidance

Aimed at the safety and stability problems of intelligent vehicles under extreme conditions such as low adhesion road surface and emergency lane change and obstacle avoidance, this article designs a lane change and obstacle avoidance controller based on road adhesion coefficient. Using the nonlinear vehicle dynamics model as the prediction model, using the recursive least squares method to identify the road adhesion coefficient, considering the road adhesion coefficient to plan and adjust in the obstacle avoidance path as well as limit constraint conditions of the model predictive control controller, using model predictive control method for the expectation of intelligent vehicle trajectory tracking, travels tremendously guarantee the security and stability of driving. The joint CarSim–Simulink simulations results show that under poor road conditions, the trajectory tracking accuracy after optimization is higher and the vehicle is less prone to sideslip and instability. The lane change controller designed in this article has strong adaptability to different road surface adhesion coefficient, and all parameters can be controlled within a reasonable safety range at different speeds, with good robustness.

Download Full-text

Trajectory Tracking of Wheeled Soccer Robots Based on Model Predictive Control

2021 6th International Conference on Control and Robotics Engineering (ICCRE) ◽

10.1109/iccre51898.2021.9435731 ◽

2021 ◽

Author(s):

Mingze Xu ◽

Bo Liu ◽

Jun Zhou ◽

Zelin Zhou ◽

Zhenjie Weng

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Trajectory Tracking ◽

Soccer Robots

Download Full-text

Compensatory model predictive control for post-impact trajectory tracking via active front steering and differential torque vectoring

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/0954407020979087 ◽

2020 ◽

pp. 095440702097908

Author(s):

Mingcong Cao ◽

Chuan Hu ◽

Rongrong Wang ◽

Jinxiang Wang ◽

Nan Chen

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Trajectory Tracking ◽

Tracking Error ◽

Collision Probability ◽

Critical Conditions ◽

Physical Analysis ◽

Compensatory Model ◽

Torque Vectoring ◽

Post Impact

This paper investigates the trajectory tracking control of independently actuated autonomous vehicles after the first impact, aiming to mitigate the secondary collision probability. An integrated predictive control strategy is proposed to mitigate the deteriorated state propagation and facilitate safety objective achievement in critical conditions after a collision. Three highlights can be concluded in this work: (1) A compensatory model predictive control (MPC) strategy is proposed to incorporate a feedforward-feedback compensation control (FCC) method. Based on the definite physical analysis, it is verified that adequate reverse steering and differential torque vectoring render more potentials and flexibility for vehicle post-impact control; (2) With compensatory portions, the deteriorated states after a collision are far beyond the traditional stability envelope. Hence it can be further manipulated in MPC by constraint transformation, rather than introducing soft constraints and decreasing the control efforts on tracking error; (3) Considering time-varying saturation on input, input rate, and slip ratio, the proposed FCC-MPC controller is developed to improve faster deviation attenuation both in lateral and yaw motions. Finally two high-fidelity simulation cases implemented on CarSim-Simulink conjoint platform have demonstrated that the proposed controller has the advanced capabilities of vehicle safety improvement and better control performance achievement after severe impacts.

Download Full-text