Risk and comfort management for multi-vehicle navigation using a flexible and robust cascade control architecture

This paper presents a new cascade control architecture formulation for addressing the problem of autonomous vehicle trajectory tracking under risk and comfort constraints. The integration of these constraints has been split between an inner and an outer loop. The former is made of a robust controller dedicated to stabilizing the car dynamics while the latter uses a nonlinear Model Predictive Control (MPC) scheme to control the car trajectory. The proposed structure aims to take into account several important aspects, such as robustness considerations and disturbance rejection (inner loop) as well as control signal and state constraints, tracking error monitoring and tracking error prediction (outer loop). The proposed design has been validated in simulation while comparing mainly with common kinematic trajectory controllers.