Adaptive Model Predictive Control with online parameter learning during spacecraft proximity operations

This paper presents a novel Model Predictive Control (MPC), developed by PhD students, that integrates online parameter learning to manage dynamic systems. In this adaptive MPC, system parameters are continuously updated using a custom gradient-based function. This function makes real-time adjustments to critical parameters based on current state errors and system feedback, enabling a dynamic adaptation to changes. Simulation are conducted on a simulator developed over the years by students of different grade Results show the effectiveness of this approach, in terms of control accuracy, robustness, and adaptability in evolving conditions, making it a promising solution for real-time control applications in complex and uncertain environments.