Nonconvex Activation Noise-Suppressing Neural Network for Time-Varying Quadratic Programming: Application to Omnidirectional Mobile Manipulator

Abstract

This article proposes an improved general zeroing neural network model to suppress noise and to enhance the real-time performance of solving TVQP problems. The proposed model allows nonconvex activation functions and has noise suppression characteristics, i.e., the NCNSZNN model. Theoretical analyses show that the developed NCNSZNN model converges globally to an accurate solution to the TVQP problem and is robust in the case of MN. Illustrative examples and comparisons are supplied to verify the validity and superiority of the proposed model for online solving TVQP constrained by EAI with MN.