New discrete-time zeroing neural network for solving time-variant underdetermined nonlinear systems under bound constraint

Many industrial applications result in the constrained underdetermined nonlinear system (UNS) that needs to be solved. The existing solutions are reported to the constrained UNS with time-invariant coef-ficients, and they may not perform well for the time-variant case. In this paper, we attempt to address the above limitation by providing a new discrete-time zeroing neural network (DTZNN) for solving the time -variant UNS (TVUNS) under bound constraint. Specifically, the bound-constrained TVUNS (BC-TVUNS) is first transformed into a mixed nonlinear system by introducing a nonnegative variable. Then, a continuous-time ZNN (CTZNN) model is established to solve such a mixed system as well as the BC-TVUNS. By employing a special difference formula to discretize the CTZNN model, the new DTZNN model is thus proposed to obtain the solution of the BC-TVUNS. Theoretical analysis and comparative numerical results are presented to validate the effectiveness of the proposed DTZNN model over the previous DTZNN models. The DTZNN application potential is further indicated on the basis of the simulations on a dual-arm redundant robot using the proposed model. (c) 2021 Elsevier B.V. All rights reserved.