2-DOF Azimuth Thruster

1. Background & Problem Definition

• Project Name: Empirical Modeling of 2-DOF Azimuth Thruster based on Signal Compression Method
• Research Necessity:
  • Existing underwater robots use multiple fixed thrusters, resulting in large volume and weight. To solve this, a 2-DOF Gimbal Mechanism capable of changing direction with a single thruster is needed.
  • Underwater propulsion systems have complex hydrodynamic characteristics, making Analytical Modeling extremely difficult.
  • Traditional frequency response experiments struggle to obtain high-frequency data, limiting the establishment of precise control models.
• Core Objective: Derive an Empirical Mathematical Model covering the entire frequency range (low to high) through actual hardware experiments.

2. Deep Dive into Technologies

A. Mechatronics Hardware Engineering

• 2-DOF Gimbal Mechanism Design:
  • Designed a structure that freely controls the thrust direction using two servo motors (Yaw, Pitch).
  • Manufactured and mounted a Counter Mass with the same shape and mass (1.35kg) as the servo motor to resolve center of gravity imbalance.
• Underwater Waterproofing Solution:
  • Applied O-rings to all fastening parts and used Rotary Seals on rotating shafts to block fluid ingress.
  • Applied Cable Glands to prevent water intrusion into motor wires.
• Electronic Component Configuration:
  • Thruster: Maxon MI60 (Max Thrust 7kgf, Rated 24V).
  • Controller: NI Compact RIO 9049 (Real-time Controller).
  • Sensor: 6-axis F/T sensor (ROBOTUS RFT80-6A01) placed at the top to measure reaction force and moment.

B. System Identification & Signal Processing

• Signal Compression Method (SCM) Application:
  • Principle: Based on the theory that applying an impulse signal to a system reveals the entire frequency response, but uses an Equivalent Impulse Signal instead of an actual impulse to avoid damaging the physical system.
  • Algorithm Implementation:
    1. Generate time-domain test signal (Frequency band and time delay parameters set to a=450, b=2100).
    2. Convert to frequency domain via FFT (Fast Fourier Transform) and apply phase delay filter.
    3. Compress and restore the system output to time-domain impulse response via IFFT (Inverse Fourier Transform).
  • Advantage: Separates linear and non-linear components in a system containing non-linearities, allowing effective estimation of only linear system parameters.

C. Data Analysis & Modeling

• Experimental Data Processing:
  • Performed a total of 180 experiments varying thruster output (PWM 55%~95%) and rotation axes (1-axis, 2-axis).
  • Performed coordinate transformation and position compensation using Homogeneous Transformation Matrix since Sensor Frame and Moving Frame positions differ.
• Transfer Function Derivation:
  • Analyzed the slope of the measured Bode Plot to determine the system Order.
  • Calculated coefficients (k, a, b, c, d, e, g) minimizing error between experimental data (Raw Data) and model using MATLAB's fmincon optimization technique via Curve Fitting.

3. Results & Achievements

• Model Accuracy Verification:
  • Cross-correlation Coefficient analysis between derived transfer function model and actual experimental data:
    • Mz,1 (Z-axis Moment): 0.83 (Very High Accuracy).
    • Fy,1 (Y-axis Force): 0.75.
    • Fx,1 (X-axis Force): 0.60.
• Error Analysis & Engineering Insight:
  • Gimbal Inertia: The low correlation coefficient for Fx,1 is due to the large inertia of the gimbal structure causing non-linear overshoot and vibration during rotation.
  • Gravity & Buoyancy Disturbance: Identified that model tracking performance for Fz dropped slightly due to the combined effects of gravity and buoyancy.
• Conclusion: Confirmed that the proposed 2-axis propulsion system contributes to the Compact Design of underwater robots, and suggested that lightweight gimbal design is essential for future control performance improvement.

4. References & Links

• Research Paper (PDF): [Download/View PDF](/assets/신호압축법을 이용한 2자유도 애지머스 추진기의 실증적 모델.pdf)
• RISS Research Information: View on RISS