Lin Xiao

TL;DR: Based on a new evolution formula, a novel finite-time recurrent neural network is proposed and studied for solving a nonstationary Lyapunov equation and the FTZNN model is successfully applied to online tracking control of a wheeled mobile manipulator.

TL;DR: In this paper, a distributed scheme is proposed for the cooperative motion generation in a distributed network of multiple redundant manipulators that can simultaneously achieve the specified primary task to reach global cooperation under limited communications among manipulators and optimality in terms of a specified optimization index of redundant robot manipulators.

TL;DR: A robust and fixed-time zeroing neural dynamics model is proposed and analyzed for time-variant nonlinear equation (TVNE), and comparative results demonstrate the effectiveness, robustness, and advantage of the RaFT-ZND model for solving TVNE.

TL;DR: Compared with existing recurrent neural networks, the proposed two nonlinear recurrent networks have a better convergence property (i.e., the upper bound is lower), and thus the accurate solutions of general time-varying LMEs can be obtained with less time.

TL;DR: Computer simulation comparisons between the fixed-parameter neural networks and the proposedVP-CDNN via using different kinds of activation functions demonstrate that the proposed VP- CDNN has better convergence and robustness properties.