Robot Manipulator Control Using a Robust Data-Driven Method

Robotic manipulators with diverse structures find widespread use in both industrial and medical applications. Therefore, designing an appropriate controller is of utmost importance when utilizing such robots. In this research, we present a robust data-driven control method for the regulation 2-degree-of-freedom (2-DoF) robot manipulator. The nonlinear dynamic model 2-DoF arm linearized using Koopman theory. data mode decomposition (DMD) applied to generate operator. A fractional sliding (FOSMC) employed govern model. We compare performance (KFOSMC) conventional proportional integral derivative (PID) FOSMC prior linearization by results demonstrate that KFOSMC outperforms PID terms high tracking performance, low error, minimal signals.