A software framework for stochastic model predictive control of nonlinear continuous-time systems (GRAMPC-S)

TL;DR

GRAMPC-S is an open-source framework enabling stochastic model predictive control of nonlinear uncertain systems, incorporating uncertainty propagation and Gaussian process regression to handle unknown dynamics, with practical millisecond sampling capabilities.

Contribution

The paper introduces GRAMPC-S, a novel open-source stochastic MPC framework that integrates multiple uncertainty propagation methods and Gaussian process regression for nonlinear systems.

Findings

Effective control of nonlinear uncertain systems demonstrated.

Framework operates with millisecond sampling times.

Versatile application across various technical domains.

Abstract

This paper presents the open-source stochastic model predictive control framework GRAMPC-S for nonlinear uncertain systems with chance constraints. It provides several uncertainty propagation methods to predict stochastic moments of the system state and can consider unknown parts of the system dynamics using Gaussian process regression. These methods are used to reformulate a stochastic MPC formulation as a deterministic one that is solved with GRAMPC. The performance of the presented framework is evaluated using examples from a wide range of technical areas. The experimental evaluation shows that GRAMPC-S can be used in practice for the control of nonlinear uncertain systems with sampling times in the millisecond range.