182 citations · 1k across the 67 of their papers we have counts for
20 papers · 1 filter
Design of periodic scheduling and control for networked systems under random data loss
Atreyee Kundu, Daniel E. Quevedo
This paper deals with Networked Control Systems (NCSs) whose shared networks have limited communication capacity and are prone to data losses. We assume that among (N) plants, only…
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…
Sparse and Constrained Stochastic Predictive Control for Networked Systems
Prabhat K. Mishra, Debasish Chatterjee, Daniel E. Quevedo
This article presents a novel class of control policies for networked control of Lyapunov-stable linear systems with bounded inputs. The control channel is assumed to have i.i.d. B…
Co-design of jump estimators and transmission policies for wireless multi-hop networks with fading channels [Extended]
D. Dolz, D. E. Quevedo, I. Peñarrocha +2
We study transmission power budget minimization of battery-powered nodes in a remote state estimation problem over multi-hop wireless networks. Communication links between nodes ar…
Stabilizing Stochastic Predictive Control under Bernoulli Dropouts
Prabhat K. Mishra, Debasish Chatterjee, Daniel E. Quevedo
This article presents tractable and recursively feasible optimization-based controllers for stochastic linear systems with bounded controls. The stochastic noise in the plant is as…
Shaped Gaussian Dictionaries for Quantized Networked Control Systems with Correlated Dropouts
Edwin G. W. Peters, Daniel E. Quevedo, Jan Østergaard
This paper studies fixed rate vector quantisation for noisy networked control systems (NCSs) with correlated packet dropouts. In particular, a discrete-time linear time invariant s…
Anytime Control using Input Sequences with Markovian Processor Availability
Daniel E. Quevedo, Wann-Jiun Ma, Vijay Gupta
We study an anytime control algorithm for situations where the processing resources available for control are time-varying in an a priori unknown fashion. Thus, at times, processin…
Performance vs complexity trade-offs for Markovian networked jump estimators
Daniel Dolz, Daniel E. Quevedo, Ignacio Peñarrocha +1
This paper addresses the design of a state observer for networked systems with random delays and dropouts. The model of plant and network covers the cases of multiple sensors, out-…
An Optimal Transmission Strategy for Kalman Filtering over Packet Dropping Links with Imperfect Acknowledgements
Mojtaba Nourian, Alex S. Leong, Subhrakanti Dey +1
This paper presents a novel design methodology for optimal transmission policies at a smart sensor to remotely estimate the state of a stable linear stochastic dynamical system. Th…
Transmission Power Scheduling for Energy Harvesting Sensor in Remote State Estimation
Yuzhe Li, Daniel E. Quevedo, Vincent Lau +2
We study remote estimation in a wireless sensor network. Instead of using a conventional battery-powered sensor, a sensor equipped with an energy harvester which can obtain energy…
Stochastic Stability of Event-triggered Anytime Control
Daniel E. Quevedo, Vijay Gupta, Wann-Jiun Ma +1
We investigate control of a non-linear process when communication and processing capabilities are limited. The sensor communicates with a controller node through an erasure channel…
Stability of Sequence-Based Control with Random Delays and Dropouts
Daniel E. Quevedo, Isabel Jurado
We study networked control of non-linear systems where system states and tentative plant input sequences are transmitted over unreliable communication channels. The sequences are c…
Sequence-based Anytime Control
Daniel E. Quevedo, Vijay Gupta
We present two related anytime algorithms for control of nonlinear systems when the processing resources available are time-varying. The basic idea is to calculate tentative contro…
State Estimation over Sensor Networks with Correlated Wireless Fading Channels
Daniel E. Quevedo, Anders Ahlen, Karl H. Johansson
Stochastic stability for centralized time-varying Kalman filtering over a wireles ssensor network with correlated fading channels is studied. On their route to the gateway, sensor…
Maximum-Hands-Off Control and L1 Optimality
Masaaki Nagahara, Daniel E. Quevedo, Dragan Nesic
In this article, we propose a new paradigm of control, called a maximum-hands-off control. A hands-off control is defined as a control that has a much shorter support than the hori…