Robust Adaptive Iterative Learning Control for De-Icing Robot Manipulator - Ngo, Thanh Quyen; Tran, Thanh Hai (Universitas Muhammadiyah Yogyakarta, 2024)
This paper introduces a new method of controlling uncertain robot using robust adaptive iterative learning control (RAILC) to track the trajectory in iterative operation mode. This method uses a PD controller combined with gain switching and forward learning techniques to predict the desired torque of the actuator. Using the Lyapunov method, this paper presents an RAILC control scheme for an uncertain robot system with structural and unstructured properties while ensuring the stability of the closed-loop system in the domain repeat. This study believes that this new control method can advance the field of robot control, especially in dealing with structured and unstructured uncertainties. It can help improve the flexibility and performance of robotic systems in real-world applications, such as automated manufacturing, transportation services, or healthcare. At the same time, providing simulation and test results demonstrates the effectiveness of the new control method in deicing high voltage power lines for robots.