  In this article, we introduce an adaptive online model update algorithm
designed for predictive control applications in networked systems, particularly
focusing on power distribution systems. Unlike traditional methods that depend
on historical data for offline model identification, our approach utilizes
real-time data for continuous model updates. This method integrates seamlessly
with existing online control and optimization algorithms and provides timely
updates in response to real-time changes. This methodology offers significant
advantages, including a reduction in the communication network bandwidth
requirements by minimizing the data exchanged at each iteration and enabling
the model to adapt after disturbances. Furthermore, our algorithm is tailored
for non-linear convex models, enhancing its applicability to practical
scenarios. The efficacy of the proposed method is validated through a numerical
study, demonstrating improved control performance using a synthetic IEEE test
case.
