Generalized Predictive Controller Design for Compressed Air Distribution
P. Anantha Christu Raj1, P. Ramesh Kumar2, Dayanand Peter3
1Karunya Intitute of Technology & Sciences, Coimbatore, India.
2P. Ramesh Kumar, Department of Agriculture, Karunya Intitute of Technology & Sciences, Coimbatore, India.
3Dayanand Peter, Department of Food Processing Technology, Karunya Intitute of Technology & Sciences, Coimbatore, India.
Manuscript received on 30 June 2019 | Revised Manuscript received on 05 July 2019 | Manuscript published on 30 July 2019 | PP: 18-22 | Volume-8 Issue-9, July 2019 | Retrieval Number: G5331058719/19©BEIESP | DOI: 10.35940/ijitee.G5331.078919
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Abstract: Compressed air distribution system is a major source of industrial power as it is safe, reduces cost of labour and easily transmitted. A well designed system should transport compressed air from the point of production to the point of application. However certain factors like type of compressor, air quality, layout and flow rate are major attributes to a well-designed system. Air ducts area unit the foremost vital elements of such reasonably systems, they outline the most important dynamic characteristics. The unidirectional flow is sometimes assumed once modelling the gas flow through associate degree duct. The work utilization of the electrical analogy methodology by change of integrity opposition with the hypothetically determined models of capacitance and inductance that prompts loads of first-request customary differential conditions for transient investigation of equal gas streams in pipeline network. Understanding the projected order standard equation is actually loads a lot of simple than world organization ravelling the arrangement of incomplete differential conditions. The process focal points of this strategy are shown by different them and therefore the ancient techniques once connected to a scope of pipe network simulation.
Keywords: Air duct, Partial differential equations, Differential predictive control, MATLAB-Simulink.
Scope of the Article: Predictive Analysis