A Coalitional Distributed Model Predictive Control Perspective for a Cyber-Physical Multi-Agent Application.

Anca Maxim,Constantin-Florin Caruntu

doi:10.3390/s21124041

Anca Maxim, Constantin-Florin Caruntu

Open Access

https://doi.org/10.3390/s21124041

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Abstract

Following the current technological development and informational advancement, more and more physical systems have become interconnected and linked via communication networks. The objective of this work is the development of a Coalitional Distributed Model Predictive Control (C- DMPC) strategy suitable for controlling cyber-physical, multi-agent systems. The motivation behind this endeavour is to design a novel algorithm with a flexible control architecture by combining the advantages of classical DMPC with Coalitional MPC. The simulation results were achieved using a test scenario composed of four dynamically coupled sub-systems, connected through an unidirectional communication topology. The obtained results illustrate that, when the feasibility of the local optimization problem is lost, forming a coalition between neighbouring agents solves this shortcoming and maintains the functionality of the entire system. These findings successfully prove the efficiency and performance of the proposed coalitional DMPC method.

Highlights

An event-triggered mechanism designed using the forward difference of the cost function is deployed to activate the local optimization problem at each sampling time; otherwise the agents use the solutions computed in the previous sampling period
The results clearly prove the efficiency of our proposed C-Distributed Model PredictiveControl (DMPC) method in a reference tracking scenario
The algorithm was tailored for an in-chain system architecture with unidirectional communication links

Summary

Introduction

Manifold systems are modular, interconnected and have a cyber-physical setup, meaning they can be viewed as coupled physical sub-systems, which are connected via communication networks [1,2,3,4,5]. In [15], a DMPC strategy for multi-agent systems based on error upper bounds is provided This criterion is used in a min–max optimization of the cost function to minimize the communication between neighbouring agents. The consensus DMPC algorithm is designed for heterogeneous, time-varying decoupled sub-systems, connected uni-directionally with a coupled cost function

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Conclusion