Meta-Learning-Based Adaptive Stability Certificates for Dynamical Systems

The trained NLF can then be used for the stability Stability assessment of non-linear systems and ensuring their estimation of the real-world system. However, this approach safe and reliable operation are of paramount importance in will fail if the real-world system dynamics is different from any real-world engineering system. While learning-based the model used for training the NLF. At the same time, the control schemes have received a lot of attention recently, real-world system model can be different from the model estimated the lack of stability guarantees is a fundamental issue that from the collected data due to various reasons, such as prevents their wide-scale deployment in the real world. One estimation error and changes in the system parameters over standard approach to estimate the stability region of a general time. Repeating the training procedure every time whenever nonlinear system is to first find a Lyapunov function for the there is such a parametric mismatch turns impractical due system and characterize its region of attraction (ROA) as the to the unavailability of necessary data samples and the need stability region (Khalil 2015). A closed-loop system is stable to get a quick stability assessment. Thus, learning a neural in the sense of Lyapunov if the system trajectory converges Lyapunov function for a real-world system using only a small to the origin as long as the initial condition is inside the number of data samples and through a few gradient updates, ROA. The sum-of-squares approach is one popular method remains an open problem.