Numerical experiments with multistep model-predictive control approaches and sensitivity updates for the tracking control of cars

TL;DR

This paper compares various multistep nonlinear model-predictive control strategies, including sensitivity updates, for car tracking, highlighting their performance and robustness differences through numerical experiments.

Contribution

It introduces and evaluates multistep NMPC approaches with sensitivity updates as an alternative to full re-optimization for car tracking.

Findings

Sensitivity updates improve computational efficiency.

Multistep NMPC enhances robustness over single step NMPC.

Re-optimization impacts control accuracy and stability.

Abstract

The paper discusses multistep nonlinear model-predictive control (NMPC) schemes for the tracking of a car model along a given reference track. In particular we will compare the numerical performance and robustness of classic single step NMPC, multistep NMPC without re-optimization, multistep NMPC with re-optimization, and multistep NMPC with sensitivity updates instead of a full re-optimization.