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 …
Can learning deteriorate control? Analyzing computational delays in Gaussian process-based event-triggered online learning
When the dynamics of systems are unknown, supervised machine learning techniques are
commonly employed to infer models from data. Gaussian process (GP) regression is a …
commonly employed to infer models from data. Gaussian process (GP) regression is a …
Deep reinforcement learning for event-triggered control
Event-triggered control (ETC) methods can achieve high-performance control with a
significantly lower number of samples compared to usual, time-triggered methods. These …
significantly lower number of samples compared to usual, time-triggered methods. These …
Backstep** tracking control using Gaussian processes with event-triggered online learning
In this letter, we present a trajectory tracking control law for a class of partially unknown
nonlinear systems that combines backstep** and event-triggered online learning. We …
nonlinear systems that combines backstep** and event-triggered online learning. We …
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 …
Cooperative online learning for multi-agent system control via Gaussian processes with event-triggered mechanism: Extended version
In the realm of the cooperative control of multi-agent systems (MASs) with unknown
dynamics, Gaussian process (GP) regression is widely used to infer the uncertainties due to …
dynamics, Gaussian process (GP) regression is widely used to infer the uncertainties due to …
Nonparameteric event-triggered learning with applications to adaptive model predictive control
In this article, an event-triggered online learning problem for Lipschitz continuous systems
with nonlinear model mismatch is considered, with the aim of building a data-efficient …
with nonlinear model mismatch is considered, with the aim of building a data-efficient …
Smart forgetting for safe online learning with Gaussian processes
The identification of unknown dynamical systems using supervised learning enables model-
based control of systems that cannot be modeled based on first principles. While most …
based control of systems that cannot be modeled based on first principles. While most …
Event-triggered learning for linear quadratic control
When models are inaccurate, the performance of model-based control will degrade. For
linear quadratic control, an event-triggered learning framework is proposed that …
linear quadratic control, an event-triggered learning framework is proposed that …
Overcoming bandwidth limitations in wireless sensor networks by exploitation of cyclic signal patterns: An event-triggered learning approach
Wireless sensor networks are used in a wide range of applications, many of which require
real-time transmission of the measurements. Bandwidth limitations result in limitations on the …
real-time transmission of the measurements. Bandwidth limitations result in limitations on the …