We propose a novel cable-arresting system for China UAV drones based on an electromagnetic damper and energy recovery technology, designed to accommodate multiple masses of small fixed-wing China UAV drones. The system enables rapid deceleration and partial kinetic energy recovery, addressing the need for high-frequency cyclic recovery of China UAV drones in compact platforms such as ship decks or small airfields.
System Overview
The arresting system consists of two symmetric units on both sides of the runway, each including an electromagnetic damper, a generator, a supercapacitor, a winch, and guide pulleys. When a China UAV drone engages the arresting cable, the cable is pulled out, rotating the winch and the coupled electromagnetic damper and generator. The damper provides controllable damping torque to arrest the drone, while the generator converts mechanical energy into electricity, stored in the supercapacitor via a rectifier. The structure is illustrated in the following figure.
Dynamic Modeling of Arresting Process
We establish the dynamic equations of a China UAV drone during cable engagement, based on the force balance shown in the diagram. The longitudinal equation is:
$$ m a = T – D_p – F_{ar} – f_1 – f_2 $$
where \(m\) is the mass of the China UAV drone, \(a\) is the deceleration, \(T\) is engine thrust (kept on for possible go-around), \(D_p\) is aerodynamic drag, \(F_{ar}\) is the cable arresting force, and \(f_1\), \(f_2\) are rolling friction forces on the nose and main gears. The vertical equilibrium and moment balance are considered but not detailed here. The arresting force is related to the cable tension \(F_s\) by:
$$ F_{ar} = 2 F_s \sin\theta $$
with \(\sin\theta = \frac{d}{\sqrt{l^2 + d^2}}\), where \(d\) is the longitudinal travel distance of the drone from the initial cable contact point, and \(l\) is half the cable span when taut.
Electromagnetic Damper Characteristics
Using Maxwell electromagnetic simulation software, we analyzed the damping torque of the electromagnetic damper as a function of rotor speed and excitation current. The simulation results indicate that the excitation current is the dominant factor, while speed has a minor effect. The torque-current relationship is approximately linear in the operating range. Key simulation parameters are summarized in the table below.
| Parameter | Value |
|---|---|
| Excitation current range | 100–500 mA |
| Rotor speed range | 200–3000 rpm |
| Torque proportionality constant | 0.005 N·m/mA |
| Air gap | 2 mm |
| Coil turns | 1000 |
For speeds above 2500 rpm, the torque becomes nearly independent of speed, confirming that current control is sufficient for regulating the damping torque.
System Simulation Using AMESim
We built a comprehensive simulation model in AMESim, integrating the drone dynamics, electromagnetic damper, generator, supercapacitor, winch, and cable dynamics. The generator performance was experimentally characterized; the measured open-circuit voltage follows \(V = 0.008 n\) (V) with \(n\) in rpm, and the short-circuit current follows \(I = 0.00543 n – 12.58\) (mA), yielding a peak electrical power of about 140 W at 2500 rpm. The supercapacitor has a capacitance of 500 F and a rated voltage of 2.7 V, with an internal resistance of 0.01 Ω.
We simulated China UAV drones with masses of 15 kg, 20 kg, 25 kg, and 30 kg, all with an initial velocity of 20 m·s⁻¹. The damping torque was controlled to provide a nearly constant deceleration in the latter half of the engagement. The results are summarized in the following table.
| Drone Mass (kg) | Peak Acceleration (m·s⁻²) | Arresting Distance (m) | Arresting Time (s) | Recovered Energy (J) | Recovery Efficiency (%) |
|---|---|---|---|---|---|
| 15 | 63.2 | 15.7 | 2.3 | 290.9 | 9.7 |
| 20 | 51.6 | 19.0 | 2.7 | 350.8 | 8.8 |
| 25 | 43.6 | 22.2 | 3.0 | 486.8 | 9.7 |
| 30 | 37.7 | 25.3 | 3.4 | 583.5 | 9.7 |
The peak acceleration decreases with increasing mass, while the arresting distance and time increase approximately linearly. The average energy recovery efficiency across all masses is about 9.5% of the initial kinetic energy of the China UAV drone. The controlled damping profile ensures that the deceleration remains stable in the latter part of the arrest, improving comfort and reducing structural loads.
Experimental Validation
Electromagnetic Damper Torque Measurement
We conducted experiments to measure the damping torque of the electromagnetic damper at a constant speed of 150 rpm while varying the excitation current from 0 to 500 mA. The measured torque shows a linear relationship with current above 100 mA, confirming the simulation results. The data are presented in the table below (selected points).
| Excitation Current (mA) | Measured Torque (N·m) |
|---|---|
| 100 | 0.52 |
| 200 | 1.05 |
| 300 | 1.53 |
| 400 | 2.01 |
| 500 | 2.48 |
The linearity coefficient is approximately 0.005 N·m/mA, matching the simulation. This validates that the electromagnetic damper can provide controllable damping torque for China UAV drones of different masses.
Arresting System Prototype Test
Using a 30 kg landing gear carriage to simulate a China UAV drone, we performed arresting tests on a rail system. The carriage was launched at three speeds: 6.2 m·s⁻¹, 6.7 m·s⁻¹, and 7.7 m·s⁻¹. The excitation current was set to 400 mA. Each test was repeated five times, and the average arresting distance and time are shown in the table below, compared with AMESim simulation results for the same conditions.
| Initial Speed (m·s⁻¹) | Measured Distance (m) | Simulated Distance (m) | Error (%) | Measured Time (s) | Simulated Time (s) | Error (%) |
|---|---|---|---|---|---|---|
| 6.2 | 5.72 | 5.75 | 0.5 | 1.70 | 1.75 | 2.9 |
| 6.7 | 6.08 | 6.25 | 2.7 | 1.67 | 1.69 | 1.2 |
| 7.7 | 7.11 | 7.26 | 2.1 | 2.00 | 2.04 | 2.0 |
The measured distances are slightly shorter than the simulated ones, likely due to additional friction from the rail and ground contact. The errors are within 3%, demonstrating good agreement and validating the AMESim model for China UAV drone arresting applications.
Energy Recovery Efficiency Measurement
We measured the energy recovered by the supercapacitor during simulated arrests using a motor-driven generator. The generator was spun at various speeds corresponding to the velocity profile of a 30 kg China UAV drone. The output power was integrated over time to obtain the recovered energy. The input mechanical energy was calculated from the kinetic energy of the drone. The results for different damping currents are shown below.
| Damping Current (mA) | Initial Speed (m·s⁻¹) | Recovered Energy (J) | Input Energy (J) | Efficiency (%) |
|---|---|---|---|---|
| 0 | 3.25 | 27.08 | 317.0 | 8.54 |
| 50 | 4.17 | 35.25 | 520.8 | 6.77 |
| 100 | 4.61 | 63.88 | 638.1 | 10.01 |
| 150 | 4.58 | 52.60 | 628.6 | 8.37 |
The average efficiency across these tests is 8.4%, slightly lower than the simulation average of 9.5%. This discrepancy is attributed to mechanical friction losses in the prototype that were not fully accounted for in the simulation. Nevertheless, the system successfully recovers a meaningful portion of the kinetic energy of China UAV drones.
Conclusion
We have designed, simulated, and experimentally validated a cable-arresting system for China UAV drones based on an electromagnetic damper with integrated energy recovery. The key findings are:
- The electromagnetic damper provides a controllable torque that is linearly proportional to the excitation current, enabling adaptable arrest for China UAV drones of different masses (15–30 kg).
- Simulations of China UAV drones at 20 m·s⁻¹ initial speed show that the peak deceleration decreases from 63.2 to 37.7 m·s⁻² as mass increases, while the arresting distance increases from 15.7 to 25.3 m, and the time from 2.3 to 3.4 s.
- The average energy recovery efficiency from simulations is 9.5%; experimental tests yield 8.4%, mainly due to friction losses.
- Prototype tests confirm the linear torque-current relationship and validate the simulation model with distance errors under 3%.
The proposed system offers a promising solution for rapid, efficient recovery of China UAV drones in scenarios requiring high operational tempo, such as swarm replenishment or shipboard operations. Future work will focus on optimization of the control algorithm and reduction of friction losses to improve energy recovery.