Coalitional model predictive control of an irrigation canal

TL;DR

This paper introduces a hierarchical, coalition-based model predictive control scheme for large-scale systems like irrigation canals, optimizing control performance and communication costs through adaptive network topology management.

Contribution

It proposes a novel hierarchical control framework that combines decentralized MPC with dynamic network topology adjustments for improved large-scale system management.

Findings

Effective control of Dez irrigation canal demonstrated

Reduced communication costs compared to centralized control

Maintained system feasibility with soft state constraints

Abstract

We present a hierarchical control scheme for large-scale systems whose components can exchange information through a data network. The main goal of the supervisory layer is to find the best compromise between control performance and communicational costs by actively modifying the network topology. The actions taken at the supervisory layer alter the control agents' knowledge of the complete system, and the set of agents with which they can communicate. Each group of linked subsystems, or coalition, is independently controlled based on a decentralized model predictive control (MPC) scheme, managed at the bottom layer. Hard constraints on the inputs are imposed, while soft constraints on the states are considered to avoid feasibility issues. The performance of the proposed control scheme is validated on a model of the Dez irrigation canal, implemented on the accurate simulator for water systems SOBEK. Finally, the results are compared with those obtained using a centralized MPC controller.