Event-Triggered Sampling Problem for Exponential Stability of Stochastic Nonlinear Delay Systems Driven by Le´ vy Processes
Q Zhu - IEEE Transactions on Automatic Control, 2024 - ieeexplore.ieee.org
This paper mainly discusses the stabilization issue for a class of stochastic nonlinear delay
systems (SNDSs) driven by Le´ vy processes. Based on a novel event-triggered strategy and …
systems (SNDSs) driven by Le´ vy processes. Based on a novel event-triggered strategy and …
Event-triggered sliding mode control for spacecraft reorientation with multiple attitude constraints
The article addresses the event-triggered attitude control problem for spacecraft anti-
unwinding reorientation with multiple attitude constraints in the presence of external …
unwinding reorientation with multiple attitude constraints in the presence of external …
Machine learning in event-triggered control: Recent advances and open issues
Networked control systems have gained considerable attention over the last decade as a
result of the trend towards decentralised control applications and the emergence of cyber …
result of the trend towards decentralised control applications and the emergence of cyber …
Learning mixtures of linear dynamical systems
Y Chen, HV Poor - International conference on machine …, 2022 - proceedings.mlr.press
We study the problem of learning a mixture of multiple linear dynamical systems (LDSs) from
unlabeled short sample trajectories, each generated by one of the LDS models. Despite the …
unlabeled short sample trajectories, each generated by one of the LDS models. Despite the …
Event-triggered learning
The efficient exchange of information is an essential aspect of intelligent collective behavior.
Event-triggered control and estimation achieve some efficiency by replacing continuous data …
Event-triggered control and estimation achieve some efficiency by replacing continuous data …
Data sharing and compression for cooperative networked control
Sharing forecasts of network timeseries data, such as cellular or electricity load patterns, can
improve independent control applications ranging from traffic scheduling to power …
improve independent control applications ranging from traffic scheduling to power …
Model‐free self‐triggered control based on deep reinforcement learning for unknown nonlinear systems
H Wan, HR Karimi, X Luan, F Liu - International Journal of …, 2023 - Wiley Online Library
This article proposes a joint learning technique for control inputs and triggering intervals of
self‐triggered control nonlinear systems with unknown dynamics. First, deep reinforcement …
self‐triggered control nonlinear systems with unknown dynamics. First, deep reinforcement …
Probabilistic robust linear quadratic regulators with Gaussian processes
A von Rohr, M Neumann-Brosig… - Learning for Dynamics …, 2021 - proceedings.mlr.press
Probabilistic models such as Gaussian processes (GPs) are powerful tools to learn unknown
dynamical systems from data for subsequent use in control design. While learning-based …
dynamical systems from data for subsequent use in control design. While learning-based …
Integrated learning self-triggered control for model-free continuous-time systems with convergence guarantees
This paper presents an integrated self-triggered control strategy with convergence
guarantees for model-free continuous-time systems using reinforcement learning. To …
guarantees for model-free continuous-time systems using reinforcement learning. To …
Event-based switching iterative learning model predictive control for batch processes with randomly varying trial lengths
Iterative learning model predictive control (ILMPC) has been recognized as an excellent
batch process control strategy for progressively improving tracking performance along trials …
batch process control strategy for progressively improving tracking performance along trials …