Optimal Delay Assignment in Delay-Aware Control of Cyber-Physical Systems: A Machine Learning Approach

Patricia Pauli, Seyed Mehran Dibaji, Anuradha M. Annaswamy, Aranya Chakrabortty

Abstract: We address a co-design of a controller and the underlying communication network in cloud-based cyber-physical systems. When communication occurs over shared resources, delays often arise that may have destabilizing effects on the closed-loop system. In order to ensure an optimal control design in the presence of such delays, not only is it useful to have a delay-aware controller but a method by which optimal assignment of delays can be imposed on the communication network links. In this paper, we propose a delay-aware stable feedback controller that judiciously accommodates the most recent state information and a machine learning (ML) based method for determining the optimal delay assignment. This ML method consists of an offline training of a neural network whose inputs are a set of selected delays and outputs are relevant performance-optimizing metrics. The resulting neural network is shown to be capable of learning the optimal delay assignment to the various links in the communication network and therefore yielding optimal performance. The proposed method is validated using a power system case study of an IEEE 68-bus, and shown to result in a notable performance improvement where in 91% of the cases a near-optimal performance can be realized.