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Implementation model predictive control (MPC) algorithm-3 for inverted pendulum
Ekaputri C.a, Syaichu-Rohman A.a
a Department of Electrical Engineering, Bandung Institute of Technology, 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]Model Predictive Control (MPC) is based on the idea to produce control input as a solution to real-time optimization problem. Optimization itself is based on the system model. MPC is used to solve multivariable control problem which has more than one variable that may have significant effect on the process. The advantage of MPC is that MPC can works effectively within constraints of the real actuator which are relatively narrow. The disadvantage of MPC lies on its complex algorithm that needs longer time than the other controller. This paper discusses the MPC application for inverted pendulum. The algorithm used is algorithm-3 for floating point. The general guidelines are modeling the system, designing the system using MATLAB, simulating the design using MATLAB, implementing the MPC algorithm on AVR ATmega32 and analyzing the result. After the guideline have been implemented, it can be seen that MPC algorithm-3 with 9 horizon and 1 iteration of quadratic programming can return the pendulum rod at the balance point by the time external force applied. © 2012 IEEE.[/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]algorithm-3,AVR ATmega32,Balance point,Complex algorithms,Control inputs,External force,Floating points,Implementation models,Inverted pendulum,Multivariable control,Real-time optimization,System models[/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]algorithm-3,AVR ATmega32,inverted pendulum,Model Predictive Control (MPC)[/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/ICSGRC.2012.6287146[/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]