Neurodynamics-based configuration transformation with engineering application to robot manipulators using two intelligent approaches.

Before performing various tasks, it is important to transform the robot manipulator from current configuration to the desired initial configuration. Therefore, this paper proposes a jerk-level configuration transformation (JCT) strategy based on neurodynamics, avoiding the joint angle, velocity, acceleration, and jerk physical limits simultaneously. Moreover, two intelligent approaches are designed to solve the JCT strategy, namely dynamic recurrent neural network and intelligent iterative optimizer, and the convergence and computational complexity are analyzed theoretically. In terms of superiority, the JCT strategy is compared with other typical strategies by simulations of a 7-degree-of-freedom manipulator. In experimental verification for engineering applications, the JCT strategy is applied to the space manipulator on the China Space Station, and verified on the ground hardware-in-the-loop experimental system to demonstrate the effectiveness and physical realizability.