Robust Steady-State-Aware Model Predictive Control for Systems with Limited Computational Resources and External Disturbances

TL;DR

This paper develops a robust steady-state-aware MPC method that maintains low computational load while effectively handling external disturbances, demonstrated through simulations and drone experiments.

Contribution

It introduces a tube-based robust steady-state-aware MPC that does not increase online computation, enhancing disturbance rejection capabilities.

Findings

The method guarantees stability and constraint satisfaction under disturbances.

Simulation results show improved robustness compared to traditional MPC.

Experimental validation on a drone confirms practical effectiveness.

Abstract

Model Predictive Control (MPC) is a powerful control strategy; however, its reliance on online optimization poses significant challenges for implementation on systems with limited computational resources. One possible approach to address this issue is to shorten the prediction horizon and adjust the conventional MPC formulation to enlarge the region of attraction. However, these methods typically introduce additional computational load. Recently, steady-state-aware MPC has been introduced to ensure output tracking and convergence to a given desired steady-state configuration while maintaining constraint satisfaction at all times without adding extra computational load. Despite its promising performance, steady-state-aware MPC does not account for external disturbances, which can significantly limit its applicability to real-world systems. This paper aims to advance the method further by enhancing its robustness against external disturbances. To achieve this, we adopt the tube-based design framework, which decouples nominal trajectory optimization from robust control synthesis, thereby requiring no additional online computational resources. Theoretical guarantees of the proposed methodology are shown analytically, and its effectiveness is assessed through simulations and experimental studies on a Parrot Bebop 2 drone.