Agent-Based Control Systems

**References**

[1] Sirringhaus H, Kawase T, Friend R, Shimoda T, Inbasekaran M, Wu W, et al. High-resolution inkjet printing of all-polymer transistor circuits. Science. [11] Soloway D, Haley PJ, editors. Neural generalized predictive control. In: Proceedings of the 1996 IEEE

International Symposium on Intelligent

[13] Psaltis D, Sideris A, Yamamura AA.

[14] Horng J-H. Hybrid MATLAB and LabVIEW with neural network to implement a SCADA system of AC servo motor. Advances in Engineering

A multilayered neural network controller. IEEE Control Systems Magazine. 1988;**8**(2):17-21

Software. 2008;**39**(3):149-155

Control; Dearborn: IEEE; 1996

Press; 1992. pp. 65-93

[12] Hecht-Nielsen R. Theory of the backpropagation neural network. In: Neural Networks for Perception. Cambridge, Massachusetts: Academic

[2] Perl A, Reinhoudt DN, Huskens J. Microcontact printing: Limitations and achievements. Advanced Materials.

[3] Noh J, Yeom D, Lim C, Cha H, Han J, Kim J, et al. Scalability of roll-to-roll gravure-printed electrodes on plastic foils. IEEE Transactions on Electronics Packaging Manufacturing. 2010;**33**(4):

Dwivedula RV. Decentralized control of web processing lines. IEEE Transactions on Control Systems Technology. 2007;

[5] Koc H, Knittel D, De Mathelin M, Abba G. Modeling and robust control of winding systems for elastic webs. IEEE Transactions on Control Systems Technology. 2002;**10**(2):197-208

[6] Shin KH. Distributed Control of Tension in Multi-Span Web Transport Systems [thesis]. Stillwater: Oklahoma

[7] Skogestad S, Postlethwaite I. Multivariable Feedback Control: Analysis and Design. New York: Wiley;

[8] Kwakernaak H. Robust control and H∞-optimization—Tutorial paper. Automatica. 1993;**29**(2):255-273

[9] Zhou K, Doyle JC, Glover K. Robust and Optimal Control. New Jersey:

[10] Friedman J, Hastie T, Tibshirani R. The Elements of Statistical Learning: Springer Series in Statistics. New York:

State University; 1991

Prentice Hall; 1996

Springer; 2001

**224**

2007

2000;**290**(5499):2123-2126

*Control Theory in Engineering*

2009;**21**(22):2257-2268

[4] Pagilla PR, Siraskar NB,

275-283

**15**(1):106-117

**Chapter 10**

**Abstract**

**1. Introduction**

**227**

Approach

Agent-Based Control System as

Directed Communication Graph

Agent-based control systems composed of simple locally interacting controller agents with demonstrated complex group behaviour. There have been relatively few implementations of agent-based control systems, mainly because of the difficulty of determining whether simple controller agent strategies will lead to desirable collective behaviour in a large system. The aim of this chapter is to design an agentbased control system for sets of 'clustered' controller agents through proposed directed communication graph approach as potent tool for the Industry 4.0. To reach global coordination with focus on real world applications, we use cluster algorithm technique in a set of rules for assigning decision tasks to agents. The outcomes include behavioural pattern, trend of agents and multi-agents usage in rail manufacturing enterprise resource planning and supply chain management. The results of this study showed that the combination of multi-agent system has ability to interact effectively and make informed decision on the type of mainte-

a Tool towards Industry 4.0:

*Adenuga Olukorede Tijani, Mpofu Khumbulani*

nance actions, resource planning, train arrival times, etc.

entity can act and make decision on behalf of a human [5].

Industry 4.0, fuzzy-PID controller, open architecture

**Keywords:** agent-based control systems, directed communication graph,

An agent is define as a concept in the field of artificial intelligence with flexible autonomous actions including responsiveness, autonomy, pro-activeness, adaptability, mobility, veracity, situatedness, reasoning, social behaviour and learning [1–3]. An agent could be a mechanical system, a person, a smart dog, and a piece of software with an embedded control algorithm used as an intelligent controller. Agent's applications in heterogeneous distributed database [4]; or mobile software

Agent-based approach has created a platform to analyse, design, and implement

complex (software) systems [6], with design methodologies namely problemoriented, architecture-oriented and process-oriented [7]. The two promising approaches to problem-oriented agent-based design are the Gaia approach [8] and

*and Adenuga Olugbenga Akeem*
