DeepCAS: A Deep Reinforcement Learning Algorithm for Control-Aware Scheduling

We consider networked control systems consisting of multiple independent controlled subsystems, operating over a shared communication network. Such systems are ubiquitous in cyber-physical systems, Internet of Things, and large-scale industrial systems. In many large-scale settings, the size of the communication network is smaller than the size of the system. In consequence, scheduling issues arise. The main contribution of this letter is to develop a deep reinforcement learning-based control-aware scheduling (DEEPCAS) algorithm to tackle these issues. We use the following (optimal) design strategy: first, we synthesize an optimal controller for each subsystem; next, we design a learning algorithm that adapts to the chosen subsystems (plants) and controllers. As a consequence of this adaptation, our algorithm finds a schedule that minimizes the control loss. We present empirical results to show that DEEPCAS finds schedules with better performance than periodic ones.