Robust model-free control for redundant robotic manipulators based on zeroing neural networks activated by nonlinear functions

Recent studies have verified that zeroing neural network is suitable for model-free feedback control of redundant robot manipulators with excellent convergence and accuracy. Unlike previous studies using a linear activation function, this paper employs zeroing neural networks activated by nonlinear functions to control redundant robots to track desired paths without knowing kinematic models of robots, and systematically investigates the finite-time convergence and robustness of the proposed control scheme. Specifically, two nonlinear-function-activated zeroing neural networks are employed to solve the Jacobian estimation problem and trajectory tracking problem respectively. After introducing a model free control scheme generally applicable to different types of robots, theoretical analysis proves that the proposed control scheme has finite-time convergence when employing nonlinear activation functions and the tracking error will not exceed the upper bound with the bounded noise interference. Finally, simulations based on a five-link planar robot and a PUMA 560 robot reveal the finite-time convergence of the proposed control scheme and verify that nonlinear functions can effectively increase the error convergence rate and reduce the tracking error caused by noises, compared with conventional method based on linear-function-activated zeroing neural networks. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

Recent studies have shown that using zeroing neural network for model-free feedback control of redundant robot manipulators can achieve excellent convergence and accuracy. By employing nonlinear activation functions, the proposed control scheme is able to track desired paths without knowing kinematic models of robots, and systematically investigates finite-time convergence and robustness. The theoretical analysis proves that the control scheme has finite-time convergence with nonlinear activation functions and the tracking error remains below the upper bound with bounded noise interference.