Probabilistic performance validation of deep learning-based robust NMPC controllers

Abstract

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.

23 pages, 4 figures

References in corpus (3)

• Efficient representation and approximation of model predictive control laws via deep learning
• Learning an Approximate Model Predictive Controller with Guarantees
• Stochastic Model Predictive Control for Linear Systems using Probabilistic Reachable Sets

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