Accurate Trajectory Tracking with MPCC for Flapping-Wing MAVs

2026-05-07

AI Summary

This work addresses the challenge of high-precision autonomous trajectory tracking in flapping-wing aerial vehicles, which is hindered by strong coupling among lift, airspeed, and steering, as well as limited control inputs. For the first time, model predictive contouring control (MPCC) is applied to flapping-wing flight, employing arc-length-parameterized trajectories and online optimization of flight progression to eliminate dependence on predefined time parametrization. A lightweight, continuously differentiable, and compact aerodynamic dynamics model is developed to meet real-time nonlinear optimization requirements. Experimental validation on the XFly platform demonstrates successful execution of circular and three-dimensional racing trajectories, achieving average tracking errors of only 6.5–9 cm at a speed of 3 m/s—nearly an order of magnitude improvement in accuracy over existing methods.

Abstract

Flapping-wing micro aerial vehicles offer quieter and safer operation than rotary-wing drones, yet achieving precise autonomous control of bird-scale ornithopters remains challenging: lift, airspeed, and turning authority are tightly coupled and governed by only a few control inputs. Conventional cascaded controllers treat altitude, speed, and heading independently, producing persistent tracking errors during complex maneuvers, while time-parameterized trajectory tracking requires predefined speed profiles that existing methods cannot robustly produce for these coupled dynamics. We address both limitations simultaneously with a Model Predictive Contouring Control (MPCC) approach that tracks arc-length-parameterized trajectories while optimizing progress online, eliminating the need for predefined timing. However, MPCC requires a dynamical model that captures the coupled aerodynamics without exceeding the computational budget of real-time nonlinear optimization. Here, we propose a compact, continuously differentiable model that captures the dominant couplings of bird-scale ornithopters, enabling real-time predictive control. We validated the method with the XFly ornithopter flying along circular and three-dimensional racing trajectories and achieved a mean deviation from the reference trajectory between 6.5 and 9 cm at speeds up to 3 m/s, which represents an almost 10-fold improvement over prior ornithopter control methods.

Problem

• flapping-wing MAVs
• trajectory tracking
• coupled dynamics
• autonomous control
• ornithopter

Innovation

• Model Predictive Contouring Control
• flapping-wing MAVs
• arc-length parameterization
• coupled aerodynamics
• real-time nonlinear optimization