Neurodynamic Approaches to Multiple Constrained Distributed Resource Allocation With Planned or Self-Regulated Demand

In industrial intelligent manufacturing processes, restricted by resources and transmission lines, many problems can be described as distributed optimization problems that need to be solved cooperatively on the premise of resource sharing, i.e., distributed resource allocation (DRA). In this article, several neurodynamic approaches are proposed to solve multiple constrained DRAs with planned or self-regulated demand over switched communication topologies. In the problems considered, the planned demand is the well-studied fixed resource demand, and the self-regulated demand refers to the scenario where the demand of users may change unpredictably. To solve DRAs with two different demands mentioned above and the multiple coupled constraints distributedly, a finite-time tracking technique and fixed-time projection technology are utilized such that local Lagrange multipliers reach consensus in finite time. It is proved that the proposed neurodynamic approaches are able to address the coupled equality and nonlinear inequality constraints simultaneously, and the states of agents along each neurodynamic approach asymptotically converge to an optimal solution to the related problem. Finally, a smart grid application is provided to verify the effectiveness of the proposed neurodynamic approaches and demonstrate the advantage of self-regulated demand over planned demand.