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Integrating PSO Optimized LQR Controller with Virtual Sensor for Quadrotor Position Control
Anditio B.a, Andrini A.D.a, Nazaruddin Y.Y.a,b
a Department of Engineering Physics, Institut Teknologi Bandung, Instrumentation and Control Research Group, Indonesia
b National Center for Sustainable Transportation Technology, CRCS Building, Bandung, 40132, 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.Linear Quadratic Regulator is one of robust and optimal controller that mostly used for handling Multiple Input Multiple Output (MIMO) system. Although, an LQR controller can handle MIMO system, it is difficult to determine the optimal weighting matrices to achieve optimal performance. An alternative for optimizing these matrices is by introducing Particle Swarm Optimization (PSO) method. Furthermore, not all state variables of a system to be controlled are available for measurement due to lack of reliable sensors, which leads to the development of virtual sensing technology. This is another alternative in control application since it can replace actual real sensors with software approximation. In this paper, development of a PSO optimized LQR controller integrated with virtual sensing system will be introduced. The developed virtual sensor consists of a Diagonal Recurrent Neural Network (DRNN) and coupled with Extended Kalman Filter (EKF), which can estimate the unknown variables from the a priori known variables. The designed control strategy will be tested on a quadrotor model having 12 states variables. The simulation results show how the position of the quadrotor can be controlled optimally and satisfactorily. Comparison with PID based controller also confirms the superiority of the proposed control 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]Control strategies,Diagonal recurrent neural networks,Linear quadratic regulator,Optimal controller,Optimal performance,Particle swarm optimization method (PSO),Quad rotors,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’s Position,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]ACKNOWLEDGEMENT 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.2018.8511323[/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]