Robust Adaptive Model Predictive Control of Quadrotors

TL;DR

This paper demonstrates the first practical implementation of robust adaptive model predictive control (RAMPC) on a quadrotor, addressing real-world challenges like noise and computation, and validating it through simulations and physical experiments.

Contribution

It adapts RAMPC for quadrotors with a state space approach, overcoming practical issues for real-world application and validating through experiments.

Findings

Successful simulation of quadrotor control under unknown mass and wind disturbances.

Effective handling of rotor efficiency drops in control scenarios.

First practical implementation of RAMPC on a physical quadrotor.

Abstract

Robust adaptive model predictive control (RAMPC) is a novel control method that combines robustness guarantees with respect to unknown parameters and bounded disturbances into a model predictive control scheme. However, RAMPC has so far only been developed in theory. The goal of this paper is to apply RAMPC to a physical quadrotor experiment. To the best of our knowledge this is the first time that RAMPC has been applied in practice using a state space formulation. In doing so, we highlight important practical challenges such as computation of $\lambda$-contractive polytopes and dealing with measurement noise, and propose modifications to RAMPC so that it can be applied on a quadrotor. We first simulate quadrotor flight with a direct and a decoupled control architecture in different scenarios. The scenarios include: (i) an unknown mass of the quadrotor as a package delivery scenario with wind as a bounded disturbance; and (ii) all rotor efficiencies drop as a power delivery problem. We then implement these scenarios on a physical quadrotor and present the experimental results.