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Controller Designs for Dynamic Positioning System using Four Thrusters
Subarkah M.T.a, Rohman A.S.a, Hidayat S.a, Anwar A.C.a, Firdaus S.A.H.a
a School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung, West Java, 40135, 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]© 2019 IEEE.This work proposes a control system designs for dynamic positioning system (DPS) with four azimuth thruster using a linear and nonlinear control, that being compared, both with feedforward control. The linear controls are LQ optimal control and PD control while the nonlinear control is the Sliding Mode. The controller is design based on the vessel model and then the controller is simulated on Simulink and tested in a real experiment with the expected result that sliding mode control perform better than the linear one. The optimation control allocations use a lagrangian multiplier technique. Experimental control testing use a real DPS where all the subsystems are connected by mosquito MQTT and the system is supported by high accuracy of DGPS. Based on the experiment, sliding mode with wind feedforward control gives the best result with the RMS error are smaller than all the 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]Azimuth thrusters,Control allocation,Controller designs,Dynamic positioning systems,Experimental control,Lagrangian multipliers,LQ optimal control,Non linear control[/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 Allocation,Dynamic Positoining System,Feedforward Control,Linear Control,Nonlinear Control[/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/ICEEI47359.2019.8988850[/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]