Abstract
In order to facilitate practical applications, discrete-time future nonlinear neural optimization with equality constraint (DFNNOEC) is investigated. First, starting from the continuous time-varying nonlinear neural optimization with equality constraint (CTNNOEC), continuous time-varying ZNN (CTZNN) models are derived by using Zhang neural network (ZNN) method. Then, a new ten-instant Zhang time discretization (TZTD) formula with high precision is presented to develop discrete algorithms for solving DFNNOEC problem in real time. Subsequently, two ten-instant discrete-time future ZNN (TDFZNN) algorithms are developed by utilizing TZTD formula to discretize CTZNN models. In addition, theoretical analyses expound the validity of TDFZNN algorithms. Finally, experiments of a numerical example and computer simulations on UR5 manipulator are carried out to confirm the effectiveness and superiority of TDFZNN algorithms. At the same time, computer simulation and physical experiment on Kinova Jaco2 manipulator further substantiate the practicability of TDFZNN algorithms.
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