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Engine Control Unit for 2 Stroke Internal Combustion Engine on Medium Altitude Unmanned Aerial Vehicle
Nahrendra I.M.A.a, Adiprawita W.a, Susanto A.a, Hadinata S.K.a
a School of Electrical Engineering and Informatics, Institut Teknologi Bandung, 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]© 2018 IEEE.This research focused on the design and implementation of an Engine Control Unit (ECU) to control the performance of a 2 Stroke Internal Combustion Engine (ICE) for a Medium Altitude Unmanned Aerial Vehicle (UAV). The control strategy focused on controlling the Air-Fuel Ratio (AFR) of the combustion process by measuring the exhaust gas and manipulating the quantity of injected fuel by using the Electronic Fuel Injector (EFI). This method was implemented to increase the system’s reliability to operate in a medium altitude environment. There are three major sub-systems in the ECU, i.e. multi-sensors interfacing, spark and fuel injection timing, and the control for injected fuel quantity. Architecture of task used in the system was based on the super loop concept complemented with event-based interrupts for handling time-critical processes. A PID Neural Network (PIDNN) control approach was used in this research to compensate a system with black-box identification which the plant’s parameters were difficult to be obtained numerically. Thus, an adaptive controller approach would be a suitable option to compensate the system.[/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]Electronic fuel injectors,Engine control unit,Multi sensor,PID neural network,timing[/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,Electronic Fuel Injector,Engine Control Unit,Internal Combustion Engine,multi-sensors,PID Neural Network,timing[/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/ISESD.2018.8605492[/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]