Modeling and Control of a Two-Axis Stabilized Gimbal Based on Kane Method

Area of Science:

• Robotics and Control Systems
• Mechanical Engineering
• Applied Mathematics

Background:

• Classical mechanics (Newton-Euler, Lagrange) present limitations in modeling complex dynamic systems like two-axis stabilizing gimbals.
• Accurate dynamic modeling is crucial for ensuring sensor stability and pointing accuracy on moving platforms.

Purpose of the Study:

• To propose and validate the Kane method for establishing a dynamic model of a two-axis stabilizing platform.
• To design and evaluate a Novel Particle Swarm Optimization Proportion Integration Differentiation (NPSO-PID) controller for enhanced gimbal performance.

Main Methods:

• Derivation of generalized velocities, deflection velocities, and angular velocities using a generalized coordinate system.
• Analysis of generalized active and inertial forces acting on the gimbal.
• Application of the Kane equation to derive the dynamic model.
• Design of an NPSO-PID controller utilizing the Particle Swarm Optimization (PSO) algorithm.
• Simulation and comparison with a classical PID controller in MATLAB/Simulink.

Main Results:

• The Kane method provides a valid and efficient approach for dynamic modeling of two-axis stabilizing platforms.
• The NPSO-PID controller demonstrates superior object tracking performance compared to classical PID controllers.
• The NPSO-PID controller offers improved parameter optimization over traditional PSO-PID controllers.

Conclusions:

• The Kane method is a suitable alternative for modeling two-axis stabilizing gimbals, overcoming limitations of classical approaches.
• The developed NPSO-PID controller significantly enhances the pointing accuracy and stability of two-axis stabilizing platforms.