System identification-based modeling of autonomous mobile robots and performance assessment based on PİD and hybrid model predictive-PİD control strategies

The application areas of autonomous mobile robots (AMR) are expanding day by day, and the control algorithms developed for these systems play an increasingly critical role in terms of task performance. Model Predictive Control (MPC), a control method based on a mathematical system model, is becoming an important control method for AMRs because it makes optimization-based decisions by predicting dynamic system behaviour. In this context, within the scope of this study, a high-fidelity mathematical model of the system was obtained by using system identification methodologies for an AMR platform, Turtlebot3 Burger. During the system identification processes, the robot was accepted as a black box system, and no parameters in the system were intervened. No changes were made to the factory-made Proportional-Integral-Derivative (PID) controller that came with Turtlebot3. The system model was defined using only the input signals given to the system and the output signals received in return. The obtained system model was verified and validated with different test methods. Using the defined system model, an MPC was designed to control the linear and angular components of the robot's motion, and the designed controller performance was optimized within the limitations of the robot. The optimized controller was first tested in simulation and then on the real robot. The results of the tests performed on the real robot were evaluated by comparative analysis.