Robust Tube-Based Decentralized Nonlinear Model Predictive Control of an Autonomous Tractor-Trailer System

TL;DR

This paper presents a robust decentralized nonlinear model predictive control method for an autonomous tractor-trailer system, combining tube-based robustness and nonlinear estimation to achieve accurate trajectory tracking on challenging terrains.

Contribution

It introduces a novel tube-based decentralized nonlinear model predictive control framework with nonlinear moving horizon estimation for autonomous tractor-trailer systems.

Findings

High control accuracy demonstrated on bumpy grass field

Robustness against subsystem interaction neglect and disturbances

Effective trajectory tracking with decentralized control approach

Abstract

This paper addresses the trajectory tracking problem of an autonomous tractor-trailer system by using a decentralized control approach. A fully decentralized model predictive controller is designed in which interactions between subsystems are neglected and assumed to be perturbations to each other. In order to have a robust design, a tube-based approach is proposed to handle the differences between the nominal model and real system. Nonlinear moving horizon estimation is used for the state and parameter estimation after each new measurement, and the estimated values are fed to robust tube-based decentralized nonlinear model predictive controller. The proposed control scheme is capable of driving the tractor-trailer system to any desired trajectory ensuring high control accuracy and robustness against neglected subsystem interactions and environmental disturbances. The experimental results show an accurate trajectory tracking performance on a bumpy grass field.