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Application of reinforcement learning on self-tuning PID controller for soccer robot multi-agent system
El Hakim A.a, Hindersah H.a, Rijanto E.b
a School of Electrical Engineering and Informatics, Bandung Institute of Technology, Indonesia
b Research Center for Electrical Power and Mechatronics, LIPI, 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]In the soccer robot game, quickly and accurately move is a very necessary. Proportional-Integral-Derivative (PID) controllers are applied to overcome it. However, to obtain the optimal control of robots, required tuning PID of parameters (Kp, Ki, and Kd) that not easy. In this research used reinforcement learning algorithm with Q-Learning method to determine the parameters in the process of self-tuning PID control. To quantify the results that obtained, it would require a comparison between the use of RL algorithms on self-tuning PID with PID conventional usage by tuning using Ziegler-Nichols oscillation method, and compared to controls that have been implemented in the YSR-A Yujin Robotics. Test was conducted by moving the 5 robot with combination 3 robots of red and 2 white robot toward a particular point and to play with a certain angle. From the test results is known that the values obtained using the RL algorithm parameters PID control output which produces the most stable and have 1.875 faster response than using the methods applied YSR-A Yujin Robotis and have 1.5 faster response compared by using the Ziegler-Nichols oscillation method. © 2013 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 parameters,Optimal controls,PID,Proportional integral derivative controllers,Q-learning,Q-learning method,Self-tuning PID,Soccer robot[/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]PID,Q-Learning,Reinforcement learning,self-tuning PID,Soccer robot[/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/rICT-ICeVT.2013.6741546[/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]