A Novel DZNN Controller for End-Actuator Tracking and Multiobstacle Avoidance of Joint-Constrained Redundant Manipulators

Obstacle avoidance is a critical supplementary task for redundant manipulators during end-actuator tracking. While extensive research has been conducted in this area, studies that effectively address the challenge of avoiding multiple obstacles remain limited. This article introduces a concise continuous zeroing neural network (CZNN) controller, which enables the avoidance of multiple moving and stationary obstacles during end-actuator tracking with the joint-angle bound constraint considered. To enhance practical applicability, this article further develops a discrete ZNN (DZNN) controller, using the explicit linear three-step discretization formula to discretize the CZNN controller, and comprehensive theoretical analyses are provided. To verify the performance of the proposed DZNN controller, comparative simulative and physical experiments are conducted on the xArm6 manipulator. Results demonstrate that the proposed DZNN controller has superior performance and better practicability in comparison with other methods.