Iterative Learning Control of Energy Management System: Survey on Multi-Agent System Framework


  • Dinh Hoa Nguyen Toyota Technological Institute
  • David Banjerdpongchai Chulalongkorn University



Iterative learning control (ILC), energy management system (EMS), multi-agent system (MAS), building temperature control (BTC).


This paper presents a brief survey of recent works on Iterative Learning Control (ILC) of Energy Management System (EMS) based on a framework of Multi-Agent System (MAS). ILC is a control methodology which is especially suitable for dynamical systems whose control tasks are executed in a finite time interval and are repeated over and over. The key idea of ILC is to take available system information in the past and current runs, to generate the control input for the next run. EMS is a computer-based system to monitor energy consumption, control operation, and optimize energy supplies and demands. EMS can be naturally modeled as MAS since each power-generated or power-consumed component of EMS can be cast as agent. Each agent of MAS is a dynamical system itself and has its own target such as tracking desired trajectory and minimizing energy. Moreover, there are common objectives of EMS which aim to attain its energy efficiency, reliability and optimality. Then one agent can cooperate with other agents to achieve some global objectives, in addition to their own local goals, by exchanging information with other agents. Lastly, we will explore some open research problems and their potential applications.


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Author Biographies

Dinh Hoa Nguyen

Control System Laboratory, Department of Advanced Science and Technology, Toyota Technological Institute, Japan

David Banjerdpongchai

Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand


Published In
Vol 20 No 5, Nov 25, 2016
How to Cite
D. H. Nguyen and D. Banjerdpongchai, “Iterative Learning Control of Energy Management System: Survey on Multi-Agent System Framework”, Eng. J., vol. 20, no. 5, pp. 1-4, Nov. 2016.

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