Emergency obstacle avoidance trajectory tracking control based on active disturbance rejection for autonomous vehicles

Abstract

To solve the problem of understeer and oversteer for autonomous vehicle under high-speed emergency obstacle avoidance conditions, considering the effect of steering angular frequency and vehicle speed on yaw rate for four-wheel steering vehicles in the frequency domain, a feed-forward controller for four-wheel steering autonomous vehicles that tracks the desired yaw rate is proposed. Furthermore, the steering sensitivity coefficient of the vehicle is compensated linearly with the change in the steering angular frequency and vehicle speed. In addition, to minimize the tracking errors caused by vehicle nonlinearity and external disturbances, an active disturbance rejection control feedback controller that tracks the desired lateral displacement and desired yaw angle is designed. Finally, CarSim® obstacle avoidance simulation results show that an autonomous vehicle with the four-wheel steering path tracking controller consisting of feed-forward control and feedback control could not only improve the tire lateral forces but also reduce tail flicking (oversteer) and pushing ahead (understeer) under high-speed emergency obstacle avoidance conditions.

Evaluating the Impact of Sight Distance and Geometric Alignment on Driver Performance in Freeway Exits Diverging Area Based on Simulated Driving Data

“…A moderate SAR and SWA can make the driver more comfortable in the process of driving and make the workload intensity smaller, which is conducive to driving safety. Many studies have realized that the driver's SAR and SWA [61] are important factors to ensure driving safety [62,63] and these two indicators are also affected by other factors. In this study, except for the size of the radius of circular curve, other environmental conditions are the same; therefore, we can take SAR and SWAF as important measurement indexes.…”

Time-optimal obstacle avoidance of autonomous ship based on nonlinear model predictive control

“…In similar scenarios, a two-level architecture MPC controller has been widely used in obstacle avoidance and time-optimal tracking of ground vehicles (Gao et al, 2012;Frasch et al, 2013;Verschueren et al, 2014;Zhang et al, 2020a). MPC controller was adopted to solve trajectory tracking and obstacle avoidance in the fields of aerial drones and ground vehicles (Liu et al, 2020;Zhang et al, 2020b;Emami and Banazadeh, 2021). Therefore, MPC is a competitive candidate applied in GNC system with developing high-performance computation.…”