Neural Network Based Sliding Mode Control for Robotic Manipulator

Robotic manipulators are used in many applications. However, robotic arms are complex systems due to external disturbances, perturbations, and their coupled non-linear dynamics. This thesis aims to propose a robust control strategy for autonomous robotic manipulation. First, the trajectory tracking problem was introduced and an approach to overcome this issue using a sliding mode controller combined with a neural network is proposed. Then, the proposed approach is compared to classical and modern control methods including controllers from the literature to demonstrate the performance of the proposed controller. The proposed controller was then integrated with a grasp detection algorithm for an autonomous manipulation application. Simulations and hardware experiments were conducted to validate the performance of the proposed method.