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Found 19 papers, 0 papers with code
Extending direct data-driven predictive control towards systems with finite control sets
We test the reformulation on a popular electrical drive example and compare the computation times of sphere decoding FCS-DPC with an enumeration-based and a MIQP method.
Remote State Estimation with Privacy Against Active Eavesdroppers
To maintain state confidentiality, we propose an encoding scheme that is activated on the detection of an eavesdropper.
Ensemble Nonlinear Model Predictive Control for Residential Solar-Battery Energy Management
In a dynamic distribution market environment, residential prosumers with solar power generation and battery energy storage devices can flexibly interact with the power grid via power exchange.
Structure-Enhanced DRL for Optimal Transmission Scheduling
In addition, we show that the derived structural properties exist in a wide range of dynamic scheduling problems that go beyond remote state estimation.
Innovation-Based Remote State Estimation Secrecy with no Acknowledgments
Aiming to achieve a reliable state estimate for a legitimate estimator while ensuring secrecy, we propose a secrecy encoding scheme without the need for packet receipt acknowledgments.
Remote State Estimation with Privacy Against Eavesdroppers
A remote legitimate user estimates the state of a linear plant from the state information received from a sensor via an insecure and unreliable network.
Structure-Enhanced Deep Reinforcement Learning for Optimal Transmission Scheduling
Furthermore, we introduce a structure-enhanced loss function to add penalty to actions that do not follow the policy structure.
Deep Learning for Wireless Networked Systems: a joint Estimation-Control-Scheduling Approach
Wireless networked control system (WNCS) connecting sensors, controllers, and actuators via wireless communications is a key enabling technology for highly scalable and low-cost deployment of control systems in the Industry 4. 0 era.
Bayesian Quickest Change Detection of an Intruder in Acknowledgments for Private Remote State Estimation
For geographically separated cyber-physical systems, state estimation at a remote monitoring or control site is important to ensure stability and reliability of the system.
Stability Enforced Bandit Algorithms for Channel Selection in Remote State Estimation of Gauss-Markov Processes
In this paper we consider the problem of remote state estimation of a Gauss-Markov process, where a sensor can, at each discrete time instant, transmit on one out of M different communication channels.
Stability Conditions for Remote State Estimation of Multiple Systems over Semi-Markov Fading Channels
In particular, we show that, from a system stability perspective, fast fading channels may be preferable to slow fading ones.
Transmission power policies for energy-efficient wireless control of nonlinear system
We present a controller and transmission policy design procedure for nonlinear wireless networked control systems.
Deep Reinforcement Learning for Wireless Scheduling in Distributed Networked Control
We consider a joint uplink and downlink scheduling problem of a fully distributed wireless networked control system (WNCS) with a limited number of frequency channels.
Stability Conditions for Remote State Estimation of Multiple Systems over Multiple Markov Fading Channels
We investigate the stability conditions for remote state estimation of multiple linear time-invariant (LTI) systems over multiple wireless time-varying communication channels.
A Jointly Optimal Design of Control and Scheduling in Networked Systems under Denial-of-Service Attacks
We consider the joint design of control and scheduling under stochastic Denial-of-Service (DoS) attacks in the context of networked control systems.
Remote State Estimation with Smart Sensors over Markov Fading Channels
A smart sensor forwards its local state estimate to a remote estimator over a time-correlated $M$-state Markov fading channel, where the packet drop probability is time-varying and depends on the current fading channel state.
Deep reinforcement learning for scheduling in large-scale networked control systems
This work considers the problem of control and resource scheduling in networked systems.
DeepCAS: A Deep Reinforcement Learning Algorithm for Control-Aware Scheduling
The main contribution of this paper is to develop a deep reinforcement learning-based \emph{control-aware} scheduling (\textsc{DeepCAS}) algorithm to tackle these issues.
Asynchronous stochastic approximations with asymptotically biased errors and deep multi-agent learning
In this paper, we present verifiable sufficient conditions for stability and convergence of asynchronous SAs with biased approximation errors.
