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Technological Considerations for Devs-Based System Control

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Technological Considerations for Devs-Based System Control.

ABSTRACT

System control has grown from manual control where humans sense the environment, analyse the result and determine control actions to semi-automated control.

In semi-automated control sensing is done by sensing devices and with the help of communication technologies, humans at a control centre view the environment, analyse it and determine control action.

However, advances in computational and processing technologies has now shifted the most important role of making control decisions away from humans, to microcontrollers/microprocessors.

The problem now is how to make these devices intelligent enough to make the best control decision. Different control methods have been used in system control they include, the proportion integral derivative (PID) control, Model-Based control and control based on Artificial Intelligence (AI).

We proposed an architecture and explored technologies that can be used to realize Discrete Event System Specification (DEVS)-Based Control, which is a Model-Based approach.

The proposed approach is based on a simulation model rather than the optimization model used in other Model-Based system control approaches.

Using DEVS in system control, we can model physical systems to be controlled, run a fast simulation of the system based on the current state of the system and determine control actions.

Two-fold Communication issues were identified and the research addressed different methods of solving the two-fold communication problems.

INTRODUCTION

This chapter presents an overview of the research context, the motivation behind this research and the corresponding aims and objectives. It begins with a brief introduction of modeling and simulation, i.e. Discrete Event System Specification (DEVS).

This is a formalism for discrete event modeling and simulation, thereafter system control is introduced before presenting how it has evolved. Moreover,itprovidesinformationaboutthemotivationfortheresearchandthe drive which steered the research goals.

It is followed by a description of the research aims and objectives, then the approach adopted is presented. Finally, the outline of the rest of the thesis is revealed.

CONTEXT

Computational science (modeling and simulation) has become the third pillar of science along side theory and physical experiment (PITAC, 2005). Modeling and simulation (M&S) enable researchers to build and test models of complex real life systems.

They do so without conducting physical experiments, or building and testing models of phenomena that cannot be replicated in the laboratory/physically.

According to “The Theory of Modeling and Simulation” (Zeigler et al., 2000), there are four major important concepts of M&S. The concepts and relationship between them.

REFERENCES

Adegoke, A., Togo, H., & Traore, M. K. (2013). A Unifying Framework for Specifying DEVS Paralle and Distributed Simulaiton Architectures. Simulation: Trans. Of the Soc. Of Modeling and Simulaiton Int’l, 89 (11), 1293-1309.

Bai, F., Beg, S., & Khan, M. F. (2012). Controlling Home Appliances Remotely through Voice Command. International Journal of Computer Applications, 48 (17), 1-4.

Baskar, L. D., Schutter, B. D., & Hellendoorn, H. (2008). Dynamic Speed Limits and On-Ramp Metering for IVHS using Model Predictive Control. Proceedings of the 11th International IEEE Conference on Intelligent Transportation Systems, (pp. 821 – 826). Beijing.

Baskar, L. D., Schutter, B., Hellendoorn, J., & Papp, Z. (2011). Traffic Control and Intelligent Vehicle Highway Systems: A Survey. IET Intelligent Transport Systems, 5 (1), 38-52.

Baskar, L. D., Schutter, B., Hellendoorn, J., & Papp, Z. (2011). Traffic Control and Intelligent Vehicle Highway Systems: A Survey. IET Intelligent Transport Systems, 5 (1), 38-52.

Benneth, S. (1996, June). A Brief History of Automatic Control. IEEE Control Systems, 17 – 25.

CSN Team.

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