Artículo
Probabilistic performance validation of deep learning-based robust NMPC controllers
Autor/es | Karg, Benjamin
Alamo, Teodoro Lucía, Sergio |
Departamento | Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática |
Fecha de publicación | 2021-12 |
Fecha de depósito | 2022-02-04 |
Publicado en |
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Resumen | Solving nonlinear model predictive control problems in real time is still an important challenge despite of recent advances in computing hardware, optimization algorithms and tailored implementations. This challenge is ... Solving nonlinear model predictive control problems in real time is still an important challenge despite of recent advances in computing hardware, optimization algorithms and tailored implementations. This challenge is even greater when uncertainty is present due to disturbances, unknown parameters or measurement and estimation errors. To enable the application of advanced control schemes to fast systems and on low-cost embedded hardware, we propose to approximate a robust nonlinear model controller using deep learning and to verify its quality using probabilistic validation techniques. We propose a probabilistic validation technique based on finite families, combined with the idea of generalized maximum and constraint backoff to enable statistically valid conclusions related to general performance indicators. The potential of the proposed approach is demonstrated with simulation results of an uncertain nonlinear system. |
Identificador del proyecto | PID2019-106212RB-C41/AEI/10.13039/5011000110 |
Cita | Karg, B., Alamo, T. y Lucía, S. (2021). Probabilistic performance validation of deep learning-based robust NMPC controllers. International Journal of Robust and Nonlinear Control, 31 (8), 8855-8876. |
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