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Performance analysis of PID controller, fuzzy and ANFIS in pasteurization process
Maulana Y.Z.a, Hadisupadmo S.a, Leksono E.a
a Instrumentation and Control, Bandung Institute of Technology, 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]© 2016 IEEE.Pasteurization engine uses a heat exchanger to heat the milk up to 71.6 °C. Therefore, the suitable type of controller for pasteurization process must be obtained. In this paper, there are five types of controller to be compared. These controllers are a PID controller tuning Ziegler Nichols, PID tuning Cohen Coon, Cascade PID, Fuzzy Inference System and Adapative Neuro Fuzzy Inference System (ANFIS). The system is also got disturbances that is caused by changes in flow of the product and changes of a step and impulse input for the system. This controller is created in MATLAB, which is connected to the PLC using OPC. The output of MATLAB will determine the control signals which serve as an input for the heater. It is shown from the data that the system that is controlled using ANFIS has an overshoot is only 0.27 percent, which is the smallest overshoot compared to other controllers. The required power for heater is only 0.48 kW which is the smallest one compare to other controllers. The MSE (mean squared error) value after the rise time had been reached is 0.114. The MSE when the system is given the flow disturbance is 1.86. When the system’s step input is changed, the system that is controlled by ANFIS has a smaller overshoot and MSE compared to when the system is being controlled by PID cascade. However, when exposed to impulse disturbance ANFIS system controlled is highly reactive and has a lower performance than PID Cascade. Overall, it can be concluded that the performance of the ANFIS controller is better compare to other controllers.[/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]ANFIS,Flow disturbances,Fuzzy,Fuzzy inference systems,Mean squared error,Neuro-fuzzy inference systems,Performance analysis,Pid controller tuning[/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]ANFIS,Fuzzy,Heat exchanger,Pasteurization,PID,PLC[/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/ICA.2016.7811496[/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]