<![CDATA[Control systems have played an important role in the advancement of science and technology. One of the challenges is the use of conventional controllers such as PID (Proportional-Integral-Derivative) controllers which are less than optimal in dealing with complex systems. Therefore, there is a new theory of control called Repetitive Control (RC), RC allows robots to improve movement precision and repetitive task tracking. RC allows the robot to improve the precision of movement and repetitive task tracking. This research uses the Python programming language to design the control system and simulate the variation of the given reference signal and then an analysis is carried out which aims to determine the performance of control method is the most effective. The results of the Plug-in RC control method with three kinds of internal model variations namely modified, odd harmonics, and multi periods are then compared with the PID controller control method. The evaluation parameter values used in this study are time response, %overshoot, root mean square error (RMSE), integral time absolute error (ITAE). As a result, Plug-in RC is more effective in dealing with repetitive signals. For example, at plant J1 (waist) frequencies of 1 Hz and 3 Hz PID produces an ITAE value of 1875.372 and RMSE of 0.21699. Plug-in RC modified internal model produces ITAE value of 3.749 and RMSE of 0.00052. The Plug-in RC odd harmonics internal model produces an ITAE value of 5.753 and an RMSE of 0.00069. While at the frequency of multi periods the performance of the plug-in RC multi periods internal model is better than the PID controller, this can be seen in plant J2 (shoulder) where the Plug-in RC multi periods has an ITAE value of 8.993 and an RMSE of 0.00152, while the PID controller has an ITAE value of 3849.193 and an RMSE of 0.45739. Keywords— Control System, Proportional Integral Derivative (PID), Plug-in Repetitive Control, Intenal Model, Python]]>
