Event-triggered iterative learning control for output constrained multi-agent systems
Wei Cao, Huanhuan Li, Jinjie Qiao, Yi Zhu
TL;DR
This paper introduces a control strategy for multi-agent systems that reduces communication by using event-triggered learning, ensuring accurate tracking of desired outputs.
Contribution
The novelty lies in the event-triggered iterative learning control algorithm that enables consensus tracking without real-time communication.
Findings
The proposed algorithm ensures convergence using a Lyapunov function.
Simulation results confirm the effectiveness of the control protocol.
The method allows consistent tracking of desired trajectories with output constraints.
Abstract
An event-triggered iterative learning consensus tracking control strategy is proposed for output constrained nonlinear discrete-time multi-agent systems. Firstly, the estimated Pseudo partial derivative(PPD) algorithm is determined based on the input and output data of the system, and the output observer is designed based on the estimated PPD. Secondly, the deadband controller is designed based on the output estimation error of the observer, and the event trigger condition is determined by comparing the size of the output estimation error and the deadband controller function value, and the agents communicate when the trigger condition is satisfied, and do not communicate when it is not satisfied. Then, the event-triggered iterative learning control algorithm is constructed using the estimated PPD, the trigger condition and the measurement error, and the convergence of the algorithm is…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
Click any figure to enlarge with its caption.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Figure 18
Figure 19
Figure 20
Figure 21
Figure 22
Figure 23
Figure 24
Figure 25
Figure 26
Figure 27
Figure 28
Figure 29
Figure 30
Figure 31
Figure 32
Figure 33
Figure 34
Figure 35
Figure 36
Figure 37
Figure 38
Figure 39
Figure 40
Figure 41
Figure 42
Figure 43
Figure 44
Figure 45
Figure 46
Figure 47
Figure 48
Figure 49
Figure 50Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsIterative Learning Control Systems