Adaptive Noise-Learning Differential Neural Solution for Time-Dependent Equality-Constrained Quadratic Optimization

摘要

This article first proposes an adaptive noise-learning differential neural solution (ANLDNS) model, which is able to simultaneously solve the time-dependent equality-constrained quadratic optimization (TD-ECQO) problem and effectively cope with noise disturbances during the solving process. The incorporated noise learning mechanism is designed to enhance the robustness of the ANLDNS model, which is achieved by learning the variation tendency of the involved noise disturbances. Furthermore, the convergence performance and noise-learning capacity of the ANLDNS model are substantiated with theoretical proofs. Finally, the time-dependent numerical examples and an application to the control of a redundant robot are provided to demonstrate the preeminent performance and practicability of the proposed model compared with existing state-of-the-art methods.