RTI-NMPC for Control of Autonomous Vehicles Using Implicit Discretization Methods

TL;DR

This paper introduces a real-time iteration nonlinear model predictive control method using implicit discretization for autonomous vehicles, enhancing prediction accuracy and computational efficiency in stiff dynamical systems.

Contribution

It proposes a novel implicit discretization-based NMPC formulation with real-time iteration, optimized for vehicle dynamics and computational efficiency.

Findings

Effective trajectory tracking demonstrated in simulations.

Reduced computational time compared to explicit methods.

Robustness to modeling errors and external disturbances.

Abstract

Recent efforts in the development of autonomous driving technology have induced great advancements in perception, planning and control systems. Model predictive control is one of the most popular advanced control methods, but its application to nonlinear systems still depends on the development of computationally efficient methods. This work presents a nonlinear model predictive control formulation based on real-time iteration using an implicit discretization of the system's dynamics, with the objective of achieving greater prediction accuracy and lower computational cost when dealing with stiff dynamical systems, as is the case for vehicle dynamics. The proposed method is described and later evaluated on a simulation scenario considering modeling errors and external disturbances. The presented results demonstrate the effectiveness of the method when it comes to tracking a given trajectory and its low computational burden, measured in terms of execution time.