182 citations · 1k across the 67 of their papers we have counts for
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Deep Reinforcement Learning for Wireless Sensor Scheduling in Cyber-Physical Systems
Alex S. Leong, Arunselvan Ramaswamy, Daniel E. Quevedo +2
In many Cyber-Physical Systems, we encounter the problem of remote state estimation of geographically distributed and remote physical processes. This paper studies the scheduling o…
Stability analysis of event-triggered anytime control with multiple control laws
Thuy V. Dang, K. V. Ling, D. E. Quevedo
To deal with time-varying processor availability and lossy communication channels in embedded and networked control systems, one can employ an event-triggered sequence-based anytim…
DeepCAS: A Deep Reinforcement Learning Algorithm for Control-Aware Scheduling
Burak Demirel, Arunselvan Ramaswamy, Daniel E. Quevedo +1
We consider networked control systems consisting of multiple independent controlled subsystems, operating over a shared communication network. Such systems are ubiquitous in cyber-…
Output feedback stable stochastic predictive control with hard control constraints
Prabhat Kumar Mishra, Debasish Chatterjee, Daniel E. Quevedo
We present a stochastic predictive controller for discrete time linear time invariant systems under incomplete state information. Our approach is based on a suitable choice of cont…
Asynchronous stochastic approximations with asymptotically biased errors and deep multi-agent learning
Arunselvan Ramaswamy, Shalabh Bhatnagar, Daniel E. Quevedo
Asynchronous stochastic approximations (SAs) are an important class of model-free algorithms, tools and techniques that are popular in multi-agent and distributed control scenarios…