A Fuzzy-Enhanced Robust DZNN Model for Future Multiconstrained Nonlinear Optimization With Robotic Manipulator Control

Abstract

Different from the common static and continuous-time dynamic problems of unconstrained/constrained nonlinear optimization, this article aims to investigate a discrete-time dynamic problem of nonlinear optimization with multiple types of constraints, which can be succinctly termed as future multiconstrained nonlinear optimization (FMCNO) problem because of the unknown future. Considering the unique advantages of neural networks with parallelism and fuzzy control systems (FCSs) with adaptivity, a fuzzy-enhanced robust discretized zeroing neural network (FER-DZNN) model is proposed to address the FMCNO problem. Specifically, by introducing a fuzzy factor outputted from an FCS with dual inputs, the FER-DZNN model is designed on the basis of an FER evolution rule and a five-step look-ahead discretization rule. Moreover, theoretical results are provided to indicate the convergence and robustness of the FER-DZNN model under various noises. Finally, two illustrative examples, including an application example to robotic manipulator control, are presented to substantiate the superior convergent and robust performance of the FER-DZNN model under various noises for addressing the FMCNO problem.