Different-layer control of robotic manipulators based on a novel direct-discretization RNN algorithm - Guo, Jinjin; Xiao, Zhanhao; Guo, Jianhua; Hu, Xianglei; Qiu, Binbin (Neurocomputing (Amsterdam), 2025)
In this paper, the development of discrete-time recurrent neural network (RNN) algorithm is different from previous studies that require the derivation processes in the continuous-time environment. Specifically, the problem of discrete time-dependent different-layer control (DTDDLC) of robotic manipulators is investigated. To solve the DTDDLC problem, a discrete-time RNN algorithm based on direct-discretization technique is proposed, and thus named direct-discretization RNN algorithm for convenience. For comparative purposes, four indirect-discretization RNN algorithms are also developed to solve the same problem. Theoretical analyses and results manifest the convergence performance of the proposed direct-discretization RNN algorithm. Finally, numerical experiments based on a five-link planar robotic manipulator and a UR3 robotic manipulator substantiate the effectiveness and superiority of the proposed direct-discretization RNN algorithm.