Path tracking and stability integrated control of intelligent vehicles under steering collision avoidance

Abstract

In order to improve the path tracking performance and stability of intelligent vehicles during steering and collision avoidance working condition, an integrated control method combining four-wheel steering, active suspension and direct yaw-moment is proposed. Firstly, a unified chassis dynamics model with 18 degrees-of-freedom (DOF) is established, which includes steering, braking and suspension subsystems. Phase plane analysis results for both traditional 2-DOF bicycle model and the 18-DOF model are compared. The results show that although the uneven road has a certain influence on the phase portraits of sideslip angle and sideslip angular velocity of the 18-DOF model, the overall trend is consistent with those of the 2-DOF model. Secondly, a general control framework for steering and collision avoidance of autonomous vehicle is put forward, consisting of the steering control for path tracking, and active suspension and direct yaw-moment control for stability. The collision avoidance reference path is planned by the fifth-degree polynomial, and three types of steering control methods including optimal control front wheel steering, optimal control four-wheel steering and front wheel steering combined with feedforward and feedback control, are adopted for comparison. The main contribution of this paper is that the role of active suspension is considered in the path tracking and stability integrated control of intelligent vehicles under steering collision avoidance. The active suspension subsystem uses optimal control method by two different kinds of weight coefficients, which can be switched between “comfort mode” and “sport mode” based on the vehicle lateral acceleration. According to the phase portraits of vehicle center of gravity sideslip angle and sideslip angular velocity, the direct-yaw moment control system adopts proportional differential control method based on the distance from the state point to the stability boundary. Finally, simulations and comparative analysis are compared among four different control systems on high and low adhesion roads. The research results indicate that the integrated control system can improve the vehicle path tracking performance and stability significantly.

Lateral control of intelligent vehicles using radial basis function neural networks with sliding mode control based on fractional order calculus

“…Based on the above model analysis, the system state equation was established as (Lai and Huang, 2022)…”

“…Based on the above model analysis, the system state equation was established as (Lai and Huang, 2022)where X represents the system state, X=[vytrueφ˙yLεL]T, a1=−k1+k2mvx, a2=−l2k2−l1k1mvx, a3=−l2k2−l1k1Izvx, a4=−l12k1−l22k2Izvx, c1=k1m, c2=l1<...…”

Robust model-free adaptive iterative learning control for an autonomous bus trajectory tracking system

“…The research results in Li et al 12 and Han et al 13 all provided lateral control strategies; thus, a lateral motion control model was constructed. In the existing research presented by He et al and Lai and Huang, 5,6 the predictability of multiagent systems was studied. To bolster lateral control steadiness, the following control techniques have been formulated.…”

“…Gradually, classic control strategies such as sliding mode control, 3,4 integrated control, 5,6 model predictive control, 7 linear quadratic regulator (LQR) algorithms 8,9 and shared lateral controllers 10 have been developed in response to the varying control requirements imposed on autonomous vehicles.…”