Set Membership based Nonlinear Model Predictive Control

TL;DR

This paper introduces a Set Membership based NMPC approach that efficiently approximates control laws from data, reducing computational complexity for real-time autonomous vehicle control tasks.

Contribution

It proposes a novel Set Membership method to derive bounds on the NMPC control law, enabling faster optimization in nonlinear control applications.

Findings

Significantly reduced computation time in simulations

Effective control law approximation from data

Successful application to vehicle maneuvers

Abstract

We present a numerically efficient Nonlinear Model Predictive Control (NMPC) approach, called Set Membership based NMPC (SM-NMPC). In particular, a Set Membership method is used to derive from data an approximation and tight bounds on the optimal NMPC control law. These quantities are used to reduce the dimensionality and volume of the search domain of the NMPC optimization problem, allowing a significant shortening of the computation time. The proposed SM-NMPC strategy is tested in simulation, considering realistic autonomous vehicle scenarios, like parallel parking and lane keeping maneuvers.