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Adaptive-predictive control with intelligent virtual sensor
Nazaruddin Y.Y.a, Aria M.a
a Department of Engineering Physics, Instrumentation and Control Laboratory, Institut Teknologi Bandung, 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]in many industrial plants, some key variables cannot always be measured on-line and for the purpose of control, a virtual sensing system is often required. This paper is concerned with a development of an alternative intelligent control strategy, which is an integration between adaptive-predictive based controller and intelligent virtual sensing system. This allows an immeasurable variable to be inferred and used for control. The neuro-fuzzy approach is used for modelling the process as it has learning capability from the numerical data obtained from the measurements and subsequently used as process model in the generalized predictive control scheme. The intelligent virtual sensor is composed of the Diagonal Recurrent Neural Network (DRNN) and the Extended Kalman Filter (EKF) as the estimator with inputs from DRNN. The integration between virtual sensor and the controller enables the development of an on-line control scheme involving the immeasurable variable. Experimental results show some potential benefits on applying the proposed technique using the real-world process plant. Copyright © 2005 IFAC.[/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]Adaptive predictive control,Diagonal recurrent neural networks,Generalized predictive control,Intelligent control strategies,Learning capabilities,Neuro-Fuzzy,Predictive control,Virtual sensor[/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]Extended kalman filter,Intelligent control,Neuro-fuzzy,Predictive control,Real-time control,Virtual sensor[/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/10.3182/20050703-6-cz-1902.01122[/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]