Compositional Control Synthesis for Water Management System

The increased frequency and severity of extreme
weather events, challenges traditional static control strategies
for storm-water detention ponds, a critical component in urban
water management infrastructures. This paper introduces a
compositional control methodology, rooted in formal verifica-
tion and reinforcement learning, and tailored for the synthesis
of a joint optimal control strategy for the management of
distributed pond but interconnected ponds. Combining hybrid
Markov Decision Processes (HMDPs) and reinforcement learn-
ing via Uppaal Stratego, the compositional control strategy
provides a balance between fully centralized and decentralized
control strategies, both in terms of quality and computational
complexity. Based on a real-world case study we analyze
and compare the proposed methodology and show how the
synthesized strategies can control the timing and volume of
water discharge, reducing the risk of overflow caused by the
simultaneous discharge of rain water collected in multiple
ponds.