.png){kind=icon}
Return
Constrained Event-Triggered H∞ Control Based on Adaptive Dynamic Programming With Concurrent Learning
.png){kind=icon}
PRE
AI
DOI：10.1109/TSMC.2020.2997559.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this article, an event-triggered H-infinity control method is proposed based on adaptive dynamic programming (ADP) with concurrent learning for unknown continuous-time nonlinear systems with control constraints. First, a system identification technique based on neural networks (NNs) is adopted to identify completely unknown systems. Second, a critic NN is employed to approximate the value function. A novel weight updating rule is developed based on the event-triggered control law and time-triggered disturbance law, which reduces controller execution times and guarantees the stability of the system. Subsequently, concurrent learning is applied to the weight updating rule to relax the demand for the traditional persistence of excitation condition that is difficult to implement online. Finally, the comparison between the time-triggered method and event-triggered method in simulation demonstrates the effectiveness of the developed constrained event-triggered ADP method.
Keywords:
Adaptive dynamic programming (ADP)
concurrent learning
event-triggering mechanism
H-infinity control
input constraints
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
10.5
Papers:
1.1W
Citations:
5.0W
Organization
Cited Papers
ShareSave
ShareSaveIntegral reinforcement learning and experience replay for adaptive optimal control of partially-unknown constrained-input continuous-time systems.png)
AUTOMATICA
AUTOMATICAIF5.9
PREAI
ShareSaveShareSaveShareSaveMarket integration and price transmission in wheat markets of India: a transaction cost approach
PREAI
ShareSave
View more