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.
Adaptive Model Predictive Control with online parameter learning during spacecraft proximity operations / Stesina, Fabrizio; D'Ortona, Antonio; Lovaglio, Lucrezia. - ELETTRONICO. - 58:(2024), pp. 235-240. (Intervento presentato al convegno 2nd IFAC Workshop on Aerospace Control Education tenutosi a Bertinoro (Italy) nel 22-24 Luglio 2024) [10.1016/j.ifacol.2024.08.492].
Adaptive Model Predictive Control with online parameter learning during spacecraft proximity operations
Fabrizio Stesina;Antonio D'Ortona;Lucrezia Lovaglio
2024
Abstract
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.File | Dimensione | Formato | |
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https://hdl.handle.net/11583/2992035