A software framework for embedded nonlinear model predictive control using a gradient-based augmented Lagrangian approach (GRAMPC)

TL;DR

This paper introduces GRAMPC, a software framework for embedded nonlinear model predictive control that employs a gradient-based augmented Lagrangian method, enabling efficient real-time control on hardware with millisecond sampling times.

Contribution

The paper presents a revised, efficient nonlinear MPC framework tailored for embedded systems, with detailed performance analysis and hardware implementation results.

Findings

GRAMPC achieves real-time performance on embedded hardware.

It demonstrates competitive results compared to existing nonlinear MPC packages.

The framework is suitable for systems with sub-millisecond sampling times.

Abstract

A nonlinear MPC framework is presented that is suitable for dynamical systems with sampling times in the (sub)millisecond range and that allows for an efficient implementation on embedded hardware. The algorithm is based on an augmented Lagrangian formulation with a tailored gradient method for the inner minimization problem. The algorithm is implemented in the software framework GRAMPC and is a fundamental revision of an earlier version. Detailed performance results are presented for a test set of benchmark problems and in comparison to other nonlinear MPC packages. In addition, runtime results and memory requirements for GRAMPC on ECU level demonstrate its applicability on embedded hardware.