Abstract
This paper presents a model predictive control (MPC) scheme for the stabilization of high-speed autonomous ground vehicles (AGVs) considering the effect of road topography. Accounting for the road curvature and bank angle, a single-track dynamic model with roll dynamics is derived. Variable time steps are utilized for vehicle model discretization, enabling collision avoidance in the long-term without compromising the prediction accuracy in the near-term. Accordingly, safe driving constraints including the sideslip envelope, zero-moment-point and lateral safety corridor are developed to handle stability and obstacle avoidance. Taking these constraints into account, an MPC problem is formulated and solved at each step to determine the optimal steering control commands. Moreover, feedback corrections are integrated into the MPC to compensate the unmodeled dynamics and parameter uncertainties. Comparative simulations validate the capability and real-time ability of the proposed control scheme.
Highlights
As autonomous ground vehicles (AGVs) find increasing utility in both military and commercial applications, driving safety has become a critical issue that impedes their further development [1].Typically, driving safety is deemed as being collision-free, which may be adequate for earlier applications such as small robots and low-speed vehicles [2,3]
It is necessary to develop a control scheme that can systematically utilize the knowledge of vehicle dynamics and the road topography information to avoid collisions while ensuring the handling stability of high-speed AGVs
We present a novel model predictive control (MPC) scheme for stabilization control of high-speed autonomous ground vehicles considering the effects of road topography
Summary
As autonomous ground vehicles (AGVs) find increasing utility in both military and commercial applications, driving safety has become a critical issue that impedes their further development [1]. It is necessary to develop a control scheme that can systematically utilize the knowledge of vehicle dynamics and the road topography information to avoid collisions while ensuring the handling stability of high-speed AGVs. Recent work has shown that model predictive control (MPC) can be used to rigorously handle the vehicle dynamics and multiple safety constraints [4,5]. Many vehicle roll stability metrics, such as the static stability factor (SSF) [23], stability moment (SM) [24], load transfer ratio (LTR) [25], time-to-rollover (TTR) [26], zero-moment-point (ZMP) [27], etc., have been discussed While these metrics do provide important results under certain conditions, they are limited by their inherent assumptions. Simulations carried out in the MATLAB/CarSim environment validated the effectiveness and real-time ability of the proposed scheme
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