[vc_empty_space][vc_empty_space]
Estimating Immeasurable Variables of Quadrotor Using PSO Based Virtual Sensing System
Dian Andrini A.a, Anditio B.a, Nazaruddin Y.Y.a
a Department of Engineering Physics, Instrumentation and Control Research Group, 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]© 2018 IEEE.In recent years, the implementation of intelligent control systems in many control theory lead to the development of virtual sensing technologies which can predict most of the systems states accurately. Virtual sensing system or virtual sensor which consists of a Diagonal Recurrent Neural Network (DRNN) coupled with Extended Kalman Filter (EKF) will estimate the immeasurable system variables using easy to measure known variables. In this paper, virtual sensor scheme which is optimized by Particle Swarm Optimization (PSO) algorithm is introduced. The new scheme will be used to estimate the position parameters of a quadrator, which originally has all 12 state variables to be determined. Simulation studies reveals that the designed virtual sensor scheme is able to predict the positions state of the quadrotor accurately.[/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]Diagonal recurrent neural networks,Particle swarm optimization algorithm,Position parameters,Quad rotors,Simulation studies,State variables,System variables,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]Diagonal Recurrent Neural Network,Extended Kalman Filter,Particle Swarm Optimization,Quadrotor,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]Partial funding of this research is provided by the National Center for Sustainable Transportation Technology (NCSTT) under Institutional Insentive for National Center of Excellence (PUI), Indonesian Ministry of Research, Technology and Higher Education.[/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/CCTA.2018.8511558[/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]