Dissipativity-based Distributed Nonlinear Predictive Control for Cascaded Systems

Developing centralized controllers for large-scale systems, e.g. complex chemical processes, electrical power networks, is an important, yet challenging problem. Typically, it would be of advantage if one could divide the problem into smaller parts (or subproblems), and to opt for a decentralized or distributed alternative. On the other hand, due to physical interconnections between subsystems, it is often complicated to find such a solution. Additionally, performance loss or even instability of the overall system can arise if the decentralization is not done properly. It is, therefore, fundamental to develop suitable distributed/decentralized techniques in order to avoid these issues.In this paper, we propose a decentralized dissipativity-based nonlinear model predictive control strategy for cascades of physically interconnected systems, e.g cascades of hydroelectric power plants, multi-cell batteries. Under mild assumptions, we prove that the control method guarantees overall stability by independently enforcing stability of the local subsystems. This allows for efficient decentralized solutions. Furthermore, the local controllers only exchange limited information and no model of the neighbor systems is required, facilitating decoupled local controller design. Two interconnected water tanks are used to demonstrate the effectiveness of our approach.