Using a Combination of PID Control and Kalman Filter to Design of IoT-based Telepresence Self-balancing Robots during COVID-19 Pandemic

Iswanto Suwarno, Alfian Ma'arif, Nia Maharani Raharja, Tony Khristanto Hariadi, Muhammad Abdus Shomad


COVID-19 is a very dangerous respiratory disease that can spread quickly through the air. Doctors, nurses, and medical personnel need protective clothing and are very careful in treating COVID-19 patients to avoid getting infected with the COVID-19 virus. Hence, a medical telepresence robot, which resembles a humanoid robot, is necessary to treat COVID-19 patients. The proposed self-balancing COVID-19 medical telepresence robot is a medical robot that handles COVID-19 patients, which resembles a stand-alone humanoid soccer robot with two wheels that can maneuver freely in hospital hallways. The proposed robot design has some control problems; it requires steady body positioning and is subjected to disturbance. A control method that functions to find the stability value such that the system response can reach the set-point is required to control the robot's stability and repel disturbances; this is known as disturbance rejection control. This study aimed to control the robot using a combination of Proportional-Integral-Derivative (PID) control and a Kalman filter. Mathematical equations were required to obtain a model of the robot's characteristics. The state-space model was derived from the self-balancing robot's mathematical equation. Since a PID control technique was used to keep the robot balanced, this state-space model was converted into a transfer function model. The second Ziegler-Nichols's rule oscillation method was used to tune the PID parameters. The values of the amplifier constants obtained were Kp=31.002, Ki=5.167, and Kd=125.992128. The robot was designed to be able to maintain its balance for more than one hour by using constant tuning, even when an external disturbance is applied to it.


Doi: 10.28991/esj-2021-SP1-016

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Self-Balancing Robot; Telepresence Robot; Kalman Filter; COVID-19: Mathematical Modeling; PID; Zigler-Nichols.


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DOI: 10.28991/esj-2021-SP1-016


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