Optimum Allocation of Computing Resources in Networked Sensing and Control

In this paper we consider task scheduling when sensing and controlling over a network with packet dropping links. We find optimum ways of allocating limited computing resources for estimation and control of a number of linear dynamical systems with different characteristics over communication links with different qualities. We find theoretical expressions relating the optimum sampling rates of the dynamical systems to the characteristics of the communication links and dynamics of the plants. We derive optimum ways of task scheduling for two performance metrics: decay rate and the asymptotic value. The work lays the theoretical foundations for considering the impact of both limited computing and communication resources on estimation and control.