19 citations · 25 across the 6 of their papers we have counts for
7 papers · 1 filter
Behavioral Economics for Human-in-the-loop Control Systems Design: Overconfidence and the hot hand fallacy
Marius Protte, Rene Fahr, Daniel E. Quevedo
Successful design of human-in-the-loop control systems requires appropriate models for human decision makers. Whilst most paradigms adopted in the control systems literature hide t…
Stochastic Predictive Control under Intermittent Observations and Unreliable Actions
Prabhat K. Mishra, Debasish Chatterjee, Daniel E. Quevedo
We propose a provably stabilizing and tractable approach for control of constrained linear systems under intermittent observations and unreliable transmissions of control commands.…
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…
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…
Sparsity-Promoting Iterative Learning Control for Resource-Constrained Control Systems
Burak Demirel, Euhanna Ghadimi, Daniel E. Quevedo
We propose novel iterative learning control algorithms to track a reference trajectory in resource-constrained control systems. In many applications, there are constraints on the n…