A Framework and a python-package for Real-time NMPC parameters settings

TL;DR

This paper introduces a systematic framework and a Python package for tuning NMPC parameters to ensure real-time feasibility, convergence, and constraint satisfaction tailored to specific hardware and algorithms.

Contribution

It provides a structured approach for selecting NMPC design parameters and offers a Python implementation with an illustrative PVTOL aircraft example.

Findings

Framework ensures real-time implementability

Python package facilitates practical application

Demonstrated on PVTOL aircraft control

Abstract

This paper presents a framework that enables a systematic and rational choice of NMPC design components such as control updating period, down-sampling period for prediction, control parameterization, prediction horizon's length, the maximum number of iterations as well as penalties on the terminal cost and the soft constraints. The rationale that underlines the design choices is based on real-time implementability, convergence and constraints satisfaction for a given computational device and a specific optimization algorithm. Moreover, a freely available associated Python-based implementation is also described with a fully developed illustrative example implementing a nonlinear MPC controller for a Planar Vertical Take-Off and Landing (PVTOL) aircraft under control saturation and state constraints.