---
title: 'iRonCub-Mk3: Jet-Powered Aerial Humanoid'
url: https://www.emergentmind.com/topics/ironcub-mk3
type: topic
---

# iRonCub-Mk3: Jet-Powered Aerial Humanoid

iRonCub-Mk3 is a jet-powered floating-base humanoid robot built on the iCub3 platform and engineered for controlled aerial mobility using four custom-mounted turbojet engines. It constitutes one of the central research platforms for the study of humanoid flight, enabling an unprecedented integration of whole-body robot dynamics, aerodynamic modeling, advanced Model Predictive Control (MPC), and CAD-driven mechanical co-design. iRonCub-Mk3’s development and its first sustained vertical takeoff represent a salient step toward flight-ready aerial humanoids [2506.01125].

## 1. Physical Architecture and Propulsion System

iRonCub-Mk3 retains the kinematic structure of iCub3, comprising a 6-DoF free-floating base and 45 actuated joints spanning the head, torso, arms, and legs—$q \in \mathbb{R}^{6+45}$. The propulsion system consists of four JetCat P250 Pro small-scale turbojets: two are integrated into a detachable jetpack affixed to the torso and two are mounted on forearm brackets below the elbows [2506.01125]. Each engine is capable of peak static thrust ≈250 N, for a system-level thrust-to-weight ratio $T/W\approx3.4$ (robot mass $m\approx30$ kg).

Engine mounting geometry is selected to balance thrust efficacy, control authority, and thermal management; thrust axes are angled slightly (≈5–10° tilt) to optimize both attitude control and minimize structural exposure to ≈600 °C exhaust [2410.07963]. Jetpack and forearm brackets employ high-strength steel or titanium; the robot core remains an aluminum alloy. Finite Element Method (FEM) analyses with 750 N axial loads (3×max thrust) ensure minimum structural safety factors above 2.5 for primary jet interfaces. Heat shielding with aerogel is employed at critical locations [2506.01125].

A tabulation of key mechanical subsystems is provided:

| Subsystem          | Material(s)             | Safety Factor (FEM) | Unique Features                              |
|--------------------|------------------------|---------------------|-----------------------------------------------|
| Core structure     | Aluminum alloy         | —                   | iCub3 heritage                               |
| Jetpack bracket    | Steel/Titanium         | >2.5                | Custom orientation; FEM stress benchmarked    |
| Forearm support    | Steel/Titanium         | >2.5                | Adjustable geometry for thrust vector tuning  |
| Aerogel shielding  | Silica composite       | —                   | Thermal insulation ($\approx$600 °C)         |

## 2. Aerodynamic and Multibody Modeling

The dynamics are modeled using full Euler–Lagrange joint-space equations,
$$
H(q)\,\ddot{q} + C(q,\dot{q})\,\dot{q} + g(q) = \tau + J^\top F_{\rm ext},
$$
combined with centroidal momentum dynamics,
$$
\dot h = A(q)T + mge_3,
$$
where $h \in \mathbb{R}^6$ are the total linear/angular momenta, $A(q)$ is the actuator-to-centroidal wrench map, $T \in \mathbb{R}^4$ is the vector of jet thrusts, and $e_3$ is the gravity axis.

Aerodynamic modeling incorporates Computational Fluid Dynamics (CFD)-based drag and lift maps parameterized over angle of attack ($\alpha$) and sideslip ($\beta$), with force components $F_D$, $F_L$ analytically fit to the results of 45 steady RANS scenarios. The drag and normal force coefficients are mapped as
$$
C_D(\alpha,\beta) \simeq c_0 + c_1 \sin^2\alpha \sin^2\beta + c_2\sin^2\alpha + c_3\sin^2\beta,
$$
$$
C_N(\alpha,\beta) \simeq d_0 + d_1 \sin^2\alpha \sin(2\alpha)\sin^2\beta,
$$
with coefficients calibrated on CFD data for flight envelope control [2205.08301].

These aerodynamic components enter both the floating-base and centroidal equations, allowing for simulation and control of wind- and attitude-dependent force perturbations. For control purposes, aerodynamic compensation can be achieved either by direct feedback linearization using measured $v_a$ or by gain-scheduling strategies that modulate control gains under high wind disturbances [2205.08301]. Simulation results demonstrate up to 71% reduction in peak CoM tracking errors in wind when using feedback-linearizing controllers.

## 3. Thrust Estimation and Observer Design

Accurate online estimation of the turbojets' thrust is critical for flight stability and for avoiding engine saturation. iRonCub-Mk3 employs a grey-box state-space model for the turbojet shaft dynamics,
$$
J \frac{d\omega}{dt} = a_1 u - a_2 \omega^n - D \omega^2 - B \omega,
$$
where $\omega$ is shaft speed, $u$ is fuel valve command (proportional to mass-flow), and $n \approx 2.0$ models aerodynamic back-torque [2205.08330]. Thrust output is parameterized as $T(\omega) = c_1 \omega^2 + c_2 \omega + c_3$.

A nine-parameter vector $\theta$ is identified offline using high-rate rotational and thrust measurements on a custom test bench, via nonlinear least squares with Tikhonov regularization and Levenberg–Marquardt optimization. Once identified, an Extended Kalman Filter (EKF) is deployed at 1 kHz on the flight controller, ingesting only reactor angular speed to return per-turbine thrust estimates.

Bench validation shows EKF thrust error (mean absolute) within $1.8\%$ of $T_{\rm peak}$ on step and ramp profiles, and the method is robust to engine failures (e.g., error $<4$ N under compressor stall). Within the iRonCub-Mk3 control stack, the thrust observer output is used by both a model-predictive thrust allocation algorithm and stability-margin supervisor, directly informing high-level flight control and ensuring at least 20% reserve on engine capacity during maneuvers. EKF-based thrust estimation reduces altitude-tracking RMS error by 15% compared to open-loop estimates [2205.08330].

## 4. Control Architecture: MPC, Estimation, and Sensing

A key architectural element is the unified multi-rate Model Predictive Control (MPC) scheme, which linearly parameterizes the centroidal momentum equations and explicitly embeds the slow, nonlinear jet actuator dynamics [2505.16478][2506.01125]. The controller state contains CoM position, momenta, base orientation, jet thrusts and rates, and offset integrators for bias-free tracking. The principal design features are:

- **Actuation bandwidth split:** Joint position references at 1,000 Hz; turbojet throttle commands at 10 Hz [2506.01125].
- **State feedback:** Incorporates IMU (Xsens MTI-670G), vision-based position/velocity (Intel RealSense T265), and F/T sensor array (on forearms and jetpack). Fusion via UKF at up to 200 Hz for base pose and 10 Hz for thrust.
- **Thrust dynamics:** Embedded as second-order nonlinear ODEs in the prediction model, essential for realistic jet response. Control input is separated into fast (joints) and slow (jet) channels.
- **Prediction and optimization:** MPC horizon employs variable-knot discretization (N=17 over 1 s), with online QP solves via OSQP achieving average cycle time 2.18 ms (max 4.45 ms) on standard CPUs [2505.16478].
- **Cost function:** Penalizes CoM tracking error, centroidal momentum error, attitude error, control effort, and bias integrals for offset-free tracking; constraints include jet and joint limits.

In simulation, the unified MPC demonstrates robust disturbance rejection (e.g., 300 Nm torque + 50 N impulse), minimum-jerk trajectory following (CoM MAE $<$0.15 m in $z$), and ablation studies confirm that including explicit jet dynamics is necessary for stability. Single-rate MPC (no multi-rate splitting) increases position error by ≈30% and amplifies attitude oscillations by an order of magnitude [2505.16478].

## 5. CAD-Driven Co-Design and Structural Optimization

The mechanical design of iRonCub-Mk3 leverages a systematic CAD-based co-design approach to jointly optimize geometry, inertial parameters, structural safety, and flight control performance [2410.07963][2509.14935]. The primary pipeline components are:

1. **Geometric parameterization:** Key link subsystems (jetpack brackets, forearm supports) are specified by up to eight integer geometry variables (e.g., jet angle, offset, limb extension; see Table below).
2. **Objective functions:** Minimize centroidal momentum error $(\delta_h)$, joint-velocity error $(\delta_{\dot{s}})$, and time-averaged total thrust $(\delta_T)$ across flight scenarios, subject to minimum FEM safety factors (SF ≥ 10) and QP control feasibility.
3. **Structural validation:** Automated FEM on merged STL meshes quantifies von Mises stress under 250 N jet load (per turbine), with instantaneous rejection of geometries failing SF filtering.
4. **CAD–URDF integration:** Each geometry yields a fully updated URDF (collision/visual meshes, inertial tensors) for batch simulation in Gazebo or MuJoCo.

Sample results for four Pareto-optimal geometries (from [2410.07963]):

| Variant   | Angle (°) | Distance (mm) | Offset (mm) | Length (mm) | $\delta_h$ Reduction | $\delta_T$ Reduction |
|-----------|-----------|---------------|-------------|-------------|---------------------|----------------------|
| Original  | 15        | 42            | 80          | 108         | —                   | —                    |
| Optim1    | 1         | 47            | 88          | 50          | Yes                 | Yes                  |
| Optim3    | 1         | 48            | 100         | 130         | Best trade-off      | Best trade-off       |

Inertial parameter variations remain within ±10% mass and 15% principal moments. Across the 75 Pareto-optimal candidates, all achieve stress below 46.2 MPa (SF ≥ 10), and flight simulations confirm up to 20% improvement in momentum tracking and 15% reduction in thrust consumption versus baseline Mk3.

Additionally, a large-scale Design of Experiments on 5,000 CAD models, followed by k-means centroid clustering, enables multi-objective tuning of co-design parameters and control weights via NSGA-II [2509.14935]. On the 100-centroid front, dominant trade-offs are observed: variants achieve up to 80% reduction in tracking error (at +50% energy cost), or 50% energy savings (at elevated error), with several “knee” points halving both error and energy relative to the original design.

## 6. Experimental Results and System-Level Insights

Simulation and first physical takeoff tests validate the iRonCub-Mk3 platform and control approaches. Experimentally, the robot is crane-suspended on a rooftop, with tethers and heat-exclusion safety protocols. Recorded telemetric data confirms:

- **Lift-off and trajectory tracking:** Peak vertical error ≈0.15 m, lateral drift ≤0.6 m, orientation error $<$8° [2506.01125].
- **Robustness to unmodeled effects:** The multi-rate MPC tolerates significant unmodeled jet lag, wind gusts (1 m/s), and structural vibration, though IMU/F/T sensor accuracy is degraded by turbine-induced vibration.
- **Control margins:** Stability margin throughout flight is maintained (linearized MPC Hessian singular values in [0.1, 10]), and α parameter ramp throughout take-off ensures smooth transition from ground contact to aerial phase.
- **EKF/UKF-based thrust and pose estimation:** Enables closed-loop flight under substantial process and sensor noise.

Key challenges encountered include the complex nonlinear and time-varying jet dynamics (requiring possible future online parameter adaptation in the UKF), vibration-induced estimation errors, thermal effects on sensors, and sim-to-real disparities arising from unmodeled aerodynamics and ground effects.

## 7. Future Directions and Open Challenges

Ongoing work on iRonCub-Mk3 and derived platforms focuses on several fronts [2506.01125][2505.16478]:

- **Advanced estimation:** Online adaptive identification for thrust models within Kalman filters to counteract parameter drift under high-temperature/long-duration operations.
- **Sensor robustness:** Vibration isolation for inertial sensors and leverage of vision-based drift correction to mitigate mechanical/thermal noise.
- **Robust control:** Extension of MPC to robust/stochastic formulations, explicit disturbance modeling, and adaptive gain scheduling based on real-time model uncertainty.
- **Morphological expansion:** Integration of articulated hands/forearms for manipulation-in-flight scenarios, and material transitions (e.g., carbon fiber) for weight reduction and endurance.
- **Full untethered flight:** Progression toward untethered operation with onboard fuel and greater system autonomy once structural/material and estimation bottlenecks are resolved.

A *plausible implication* is that the co-design, MPC, and observer frameworks developed for iRonCub-Mk3 are broadly transferable to future jet-powered or high-thrust humanoids, providing scalable methods for structural–control integration under stringent flight constraints.

Source: https://www.emergentmind.com/topics/ironcub-mk3