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 …
Event-triggered model predictive control with deep reinforcement learning for autonomous driving
Event-triggered model predictive control (eMPC) is a popular optimal control method with an
aim to alleviate the computation and/or communication burden of MPC. However, it …
aim to alleviate the computation and/or communication burden of MPC. However, it …
Reinforcement learning-based event-triggered model predictive control for autonomous vehicle path following
Event-triggered model predictive control (MPC) has been proposed in literature to alleviate
the high computational requirement of MPC. Compared to conventional time-triggered MPC …
the high computational requirement of MPC. Compared to conventional time-triggered MPC …
Event-triggered learning
F Solowjow, S Trimpe - Automatica, 2020 - Elsevier
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 …
Learning self-triggered controllers with Gaussian processes
K Hashimoto, Y Yoshimura… - IEEE transactions on …, 2020 - ieeexplore.ieee.org
This article investigates the design of self-triggered controllers for networked control systems
(NCSs), where the dynamics of the plant are unknown a priori. To deal with the unknown …
(NCSs), where the dynamics of the plant are unknown a priori. To deal with the unknown …
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 …
Innovation-triggered Learning for Data-driven Predictive Control: Deterministic and Stochastic Formulations
Data-driven control has attracted lots of attention in recent years, especially for plants that
are difficult to model based on first-principle. In particular, a key issue in data-driven …
are difficult to model based on first-principle. In particular, a key issue in data-driven …
Statistical learning for analysis of networked control systems over unknown channels
Recent control trends are increasingly relying on communication networks and wireless
channels to close the loop for Internet-of-Things applications. Traditionally these …
channels to close the loop for Internet-of-Things applications. Traditionally these …
Safety-Critical Randomized Event-Triggered Learning of Gaussian Process With Applications to Data-Driven Predictive Control
Safety and data efficiency are important concerns in data-driven control, especially for
nonlinear systems with unknown dynamics and subject to disturbances. In this work, we …
nonlinear systems with unknown dynamics and subject to disturbances. In this work, we …
On the trade-off between event-based and periodic state estimation under bandwidth constraints
Event-based methods carefully select when to transmit information to enable high-
performance control and estimation over resource-constrained communication networks …
performance control and estimation over resource-constrained communication networks …