Real-Time Adaptive Target Tracking via Inverse Kinematics Optimization in Stabilized Systems

To keep a target item inside their field of view, two-axis gimbal systems are designed to dynamically compensate for any movements that may occur, whether such movements originate from the target itself or from the platform on which the device is mounted. This study addresses the development and optimization of inverse kinematics algorithms to enable precise, real-time target tracking in Inertial Stabilization Platforms (ISPs), also known as gimbal systems. These systems are critical for applications demanding high stability and accuracy, such as surveillance, navigation, and scientific research. The research explores the mathematical principles of inverse kinematics, highlighting the complexities of real-time processing constraints. To overcome these challenges, advanced optimization techniques are applied, focusing on minimizing computational delays and enhancing tracking precision. The proposed algorithms are implemented within a Simscape Multibody simulation framework, allowing for rigorous testing under diverse operational conditions. The validation process combines simulated environments with real-world experiments to ensure the algorithms’ robustness and practical utility. The findings reveal notable advancements in both tracking accuracy and system responsiveness, offering a pathway to more efficient and dependable gimbal systems in highly dynamic settings.