Feedback linearizing control for moving-base line-of-sight tracking systems via adaptive spherical target estimation

Area of Science:

• Robotics and Control Systems
• Signal Processing
• Estimation Theory

Background:

• Maneuvering target tracking in moving-base line-of-sight (LOS) systems is challenging due to unknown observation noise and base motion.
• Conventional methods struggle with the impact of base motion on tracking error derivatives and measurement noise variations.

Purpose of the Study:

• To develop a robust tracking system for maneuvering targets in LOS systems with unknown noise.
• To mitigate the effects of base motion and measurement noise on tracking accuracy.

Main Methods:

• A comprehensive system model integrating LOS pointing and gimbal dynamics.
• A feedback linearization control system with a disturbance observer.
• A spherical target estimation (STE) approach to decouple base motion effects.
• A variational Bayesian Kalman adaptive filter (VB-AKF) for adaptive noise variance estimation.

Main Results:

• The proposed feedback linearization control system outperforms traditional gyroscope-feedback systems.
• The STE approach effectively eliminates the coupling effect of base motion without requiring additional sensors.
• The VB-AKF reduces estimation errors caused by measurement noise variance mismatch.
• Simulations in the Webots robotic simulator confirm the superior performance of the developed methods.

Conclusions:

• The integrated approach of advanced control, STE, and VB-AKF provides a significant improvement in maneuvering target tracking for moving-base LOS systems.
• The proposed methods offer enhanced robustness and accuracy in complex, dynamic environments with uncharacterized noise.