Model Predictive Path-Following Control for a Quadrotor

2025-06-18

AI Summary

Achieving high-precision, robust path following for quadrotors under state and input constraints remains challenging. Method: This paper proposes an embedded real-time Model Predictive Control (MPC) framework. It employs a nonlinear dynamical model with a cascaded architecture to decouple position and attitude control, and introduces an adjustable “deviation corridor” instead of strict path tracking—enhancing task adaptability. A lightweight optimizer enables real-time MPC computation on resource-constrained hardware. Contribution/Results: To the best of our knowledge, this is the first successful deployment of an MPC-based path-following algorithm on the Crazyflie nano-quadrotor platform. Experiments demonstrate significantly improved tracking accuracy and robustness over conventional two-stage approaches—especially under path deviations and dynamic disturbances. The framework establishes a new paradigm for autonomous navigation of small-scale UAVs in complex environments: computationally efficient, explicitly constraint-aware, and hardware-feasible.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Machine Learning: Hardware-aware ML
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Automating drone-assisted processes is a complex task. Many solutions rely on trajectory generation and tracking, whereas in contrast, path-following control is a particularly promising approach, offering an intuitive and natural approach to automate tasks for drones and other vehicles. While different solutions to the path-following problem have been proposed, most of them lack the capability to explicitly handle state and input constraints, are formulated in a conservative two-stage approach, or are only applicable to linear systems. To address these challenges, the paper is built upon a Model Predictive Control-based path-following framework and extends its application to the Crazyflie quadrotor, which is investigated in hardware experiments. A cascaded control structure including an underlying attitude controller is included in the Model Predictive Path-Following Control formulation to meet the challenging real-time demands of quadrotor control. The effectiveness of the proposed method is demonstrated through real-world experiments, representing, to the best of the authors' knowledge, a novel application of this MPC-based path-following approach to the quadrotor. Additionally, as an extension to the original method, to allow for deviations of the path in cases where the precise following of the path might be overly restrictive, a corridor path-following approach is presented.

Problem

• Handling state and input constraints in path-following control
• Applying MPC-based path-following to quadrotors in real-time
• Allowing path deviations via corridor path-following approach

Innovation

• Model Predictive Control for quadrotor path-following
• Cascaded control structure with attitude controller
• Corridor path-following for flexible deviation handling