[vc_empty_space][vc_empty_space]
Improving performance of PID controller using artificial neural network for disturbance rejection of high pressure steam temperature control iiflndustrial boiler
Nazaruddin Y.Y.a, Azi A.N.a,b, Priatna O.a
a Instrumentation and Control Research Group, Department of Engineering Physics, Institut Teknologi Bandung, Indonesia
b Faculty of Scinence and Technology, University of Jenderal Soedriman, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]The temperature of high-pressure steam is very important to be controlled in order to perform other processes safely, especially for boiler-turbine system. Typically, PID regulator with fixed parameter is used for that purpose. However this method may usually deteriorate the control performance, particularly if the systems exhibit-highly coupled behaviour. This paper will present the integration of intelligent control technique, especially artificial neural network, to challenge some deficiencies of PID regulator in dealing with such problem. The proposed control algorithm consists of a neural network controller, which is implemented parallel to the PID controller. The presented neural network controller involves HP steam temperature and its set-point as input and error control signal as a learning signal to be minimized. The ability of proposed algorithm is tested through step-like load disturbance into boiler plant model. Remarkable results have been obtained during this disturbance test. These results showed better performance to reject the disturbances compare with the controller which involves PID regulator alone.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Artificial neural networks,Boiler plants,Control algorithms,Control performance,Error controls,Fixed parameters,High pressures,Improving performances,Load disturbances,Neural network controller,PID controller,Pid regulators,Steam temperature controls,Steam temperatures,Turbine systems[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Control performance,Intelligent control technique,Neural network controller,PID controller,Temperature of high-pressure steam[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ICCAS.2008.4694331[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]