Enhanced Orientation Tracking for Redundant Manipulators via DNN-Based Double Control
TL;DR: A novel double-indicator control scheme using a dynamic neural network (DNN) solver enhances orientation tracking and noise resistance in redundant manipulators, ensuring accurate, stable, and efficient motion control.
Abstract: This paper proposes a new double-indicator control scheme of redundant manipulators, which utilizes an untrained dynamic neural network (DNN) solver. The control scheme combines direction tracking, physical constraints, and anti-noise design to address problems of the high computational complexity and the lack of direction tracking in existing neural network-based solutions. In addition, the DNN solver provides a control-theoretic framework which ensures the global and exponential convergence, stability, and robustness of the control scheme. In our design, we specifically consider the effect of noises on the system and incorporate the anti-noise mechanism. Furthermore, the effectiveness and feasibility of the proposed control scheme are verified through simulations with a KUKA LBR iiwa 7 R800 manipulator. The results show that the DNN-based double-indicator control scheme can efficiently generate accurate motion trajectories while maintaining the directional stability of the end-effector, and can resist noises.
Submission Number: 197
Loading