Disturbance-Aware Flight Control of Robotic Gliding Blimp via Moving Mass Actuation

TL;DR

This paper tackles wind disturbance sensitivity in a lighter-than-air robotic blimp by introducing a disturbance-aware control framework that integrates a physics-based RGBlimp model, a continuum-based 2-DoF moving-mass actuator, and an onboard wind disturbance observer using moving horizon estimation (MHE). The estimated wind is fed into a model predictive controller (MPC) that coordinates the moving-mass actuation and aerodynamic effects to robustly regulate heading and attitude under varying wind conditions. Key contributions include a dedicated MHE-based wind disturbance observer for the nonlinear RGBlimp model, an MPC-based disturbance compensation scheme leveraging the moving-mass mechanism, and extensive experiments showing improved robustness and reduced tracking errors compared with open-loop and PID control. The work advances practical, disturbance-aware flight for LTA platforms, enabling safer and more reliable operation in real-world wind environments, with potential applications in environmental monitoring and autonomous inspection.

Abstract

Robotic blimps, as lighter-than-air (LTA) aerial systems, offer long endurance and inherently safe operation but remain highly susceptible to wind disturbances. Building on recent advances in moving mass actuation, this paper addresses the lack of disturbance-aware control frameworks for LTA platforms by explicitly modeling and compensating for wind-induced effects. A moving horizon estimator (MHE) infers real-time wind perturbations and provides these estimates to a model predictive controller (MPC), enabling robust trajectory and heading regulation under varying wind conditions. The proposed approach leverages a two-degree-of-freedom (2-DoF) moving-mass mechanism to generate both inertial and aerodynamic moments for attitude and heading control, thereby enhancing flight stability in disturbance-prone environments. Extensive flight experiments under headwind and crosswind conditions show that the integrated MHE-MPC framework significantly outperforms baseline PID control, demonstrating its effectiveness for disturbance-aware LTA flight.

Figures (7)

• Figure 1: Schematic of wind disturbance estimation and compensation for RGBlimp with moving mass actuation. A moving horizon estimator provides real-time wind estimates to the model predictive controller, enabling disturbance-aware heading regulation using a 2-DoF moving mass mechanism.
• Figure 2: Diagrams of the RGBlimp with continuum-based internal moving mass. (a) Overall RGBlimp with helium-filled envelope, wings, and moving mass mechanism. (b) Cable-driven continuum mechanism actuating the tip mass $\bar{m}$. (c) Relationship between 2-DoF moving mass and pitch/roll moments.
• Figure 3: Illustration of the heading adjustment mechanisms. (a) Roll-induced aerodynamic lift produces centripetal force for heading control. (b) Deflection of the continuum arm generates yaw moment via propeller thrust, enabling heading adjustment during low-speed flight.
• Figure 4: RGBlimp prototype and flight arena. The workspace utilized $x$-axis alignment for maximum flight length tracking by the OptiTrack system.
• Figure 5: Flight snapshots demonstrating robust heading control with simultaneous wind estimation and compensation under (a) headwinds and (b) crosswinds. (a) The fan generated a headwind that strengthened from 2 to 8 seconds. The robot advanced into the airflow until the aerodynamic lift balanced its net weight (6.85g), achieving near-stationary hover between 10 and 16 seconds. (b) The fan produced a crosswind from 5 to 8 seconds. The controller rapidly adjusted the moving-mass configuration to counteract lateral drift, preserving robot's heading and forward progression.