Adaptive Control Mechanisms in Space Drone Navigation

Adaptive control systems have become essential for the successful operation of space drones in the dynamic and unpredictable environment of space. These systems enable space drones to adjust their control parameters in real-time, ensuring optimal performance despite disturbances such as gravitational anomalies, solar radiation pressure, and sensor malfunctions. This paper explores the integration and application of adaptive control in space drone navigation, highlighting key techniques such as Model Reference Adaptive Control (MRAC), Self-Tuning Regulators (STR), and Robust Adaptive Control. These techniques allow space drones to autonomously adjust to changing conditions, such as variations in gravitational fields or unexpected system failures, ensuring the successful execution of missions. The paper also examines the role of advanced technologies such as machine learning, reinforcement learning, and hybrid adaptive control in enhancing the adaptability and efficiency of space drone systems. Additionally, real-world applications, such as spacecraft navigation, satellite attitude control, and space debris removal, are discussed, emphasizing the practical benefits of adaptive control in these areas. With the increasing complexity of space missions and the demand for autonomous systems, adaptive control will continue to play a pivotal role in shaping the future of space exploration. As the field progresses, the integration of AI, quantum-based systems, and predictive control will further enhance the performance and reliability of space drones, enabling more efficient and robust space operations. This paper highlights the significance of adaptive control in overcoming the challenges of space environments and providing reliable solutions for autonomous space exploration.

Adaptive Control Mechanisms in Space Drone Navigation Keywords:

Adaptive Control , Space Drones , Navigation Systems , Autonomous Systems