Deep Adaptive Control with Frequency Modulation for Aerospace Robotic Manipulators in Dynamic Object Transportation

Achieving precise performance in aerospace robotics, particularly when handling unknown payloads in uncertain environments, remains a significant challenge. Traditional model-based control methods are often impractical due to the dynamic nature of aerospace operations. Adaptive control has emerged as a viable alternative, offering the ability to compensate for modeling errors and disturbances. However, conventional adaptive schemes rely on high-gain learning rates to ensure rapid adaptation, which can induce high-frequency oscillations and instability, compromising system safety and precision. To address these limitations, we propose a novel adaptive control framework tailored for nonlinear aerospace robotics applications. Our approach refines the adaptive update law to suppress destabilizing high-frequency components while preserving robust error dynamics. This ensures stable performance even at high-gain learning rates, enabling precise and secure manipulation of dynamic objects in space. Numerical simulations in a zero-gravity environment validate the effectiveness of our method in robot manipulation tasks. Results demonstrate superior performance over conventional approaches, achieving enhanced stability and precision. Our findings contribute to the advancement of adaptive control theory and provide a reliable solution for high-performance aerospace robotics, paving the way for safer and more efficient space missions.