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

Abstract

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.

Advanced post‐impact safety and stability control for electric vehicles

“…The controller verification was done on the Carsim–Simulink platform as real crash experiments were dangerous. Study [48] proposed a feedforward‐feedback control scheme to compensate the MPC. Faster attenuation for lateral and yaw motion were achieved upon the proposed controller.…”

Post-Impact Stabilization during Lane Change Maneuver

“…The first approach involves an activated controller with collision detection using pure signal processing of sensor measurements [1][2][3][4]. The second approach estimates collision forces resulting from inter-vehicle collisions to detect the occurrence of collisions, and it utilizes these collision forces in the control system [5][6][7][8][9][10]. Methods assuming the vehicle states of counterpart vehicles are available.…”

“…In previous research, Model Predictive Control (MPC) has been extensively utilized as a controller [8,9,11,12,15], proving to be an effective method for post-collision vehicle state stabilization by predicting the vehicle's state after a collision. MPC requires future information for its implementation.…”

Model Predictive Controller Design Based on Residual Model Trained by Gaussian Process for Robots

“…Luo et al [12] designed a heterogeneous robust MPC with modelling uncertainty and external disturbances. Cao et al [13] further control the tracking error through constraint transformation in MPC to reduce the workload of tracking error control. In [14], an MPC planning controller is proposed to enable path planning systems for autonomous vehicles to cleanly handle different obstacles and road structures while exploiting vehicle dynamics for optimal path planning.…”