scholarly journals Data Driven Distributed Bipartite Consensus Tracking for Nonlinear Multiagent Systems via Iterative Learning Control

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 144718-144729
Author(s):  
Huarong Zhao ◽  
Li Peng ◽  
Hongnian Yu
Keyword(s):  
Multiagent Systems ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Data Driven ◽  
Consensus Tracking ◽  
Bipartite Consensus

scholarly journals Consensus Tracking of Fractional-Order Multiagent Systems via Fractional-Order Iterative Learning Control

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Shuaishuai Lv ◽  
Mian Pan ◽  
Xungen Li ◽  
Qi Ma ◽  
Tianyi Lan ◽  
...  
Keyword(s):  
Multiagent Systems ◽  
Fractional Order ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Convergence Conditions ◽  
Consensus Tracking ◽  
Communication Topology ◽  
Fixed Topology ◽  
Virtual Leader

In this work, the consensus problem of fractional-order multiagent systems with the general linear model of fixed topology is studied. Both distributed PDα-type and Dα-type fractional-order iterative learning control (FOILC) algorithms are proposed. Here, a virtual leader is introduced to generate the desired trajectory, fixed communication topology is considered, and only a subset of followers can access the desired trajectory. The convergence conditions are proved using graph theory, fractional calculus, and λ norm theory. The theoretical analysis shows that the output of each agent completely tracks the expected trajectory in a limited time as the iteration number increases for both PDα-type and Dα-type FOILC algorithms. Extensive numerical simulations are given to demonstrate the feasibility and effectiveness.

Consensus Tracking by Iterative Learning Control for Linear Heterogeneous Multiagent Systems Based on Fractional-Power Error Signals

Algorithms ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 185 ◽  
Author(s):  
Yu-Juan Luo ◽  
Cheng-Lin Liu ◽  
Guang-Ye Liu
Keyword(s):  
Multiagent Systems ◽  
Iterative Learning Control ◽  
Tracking Error ◽  
Fractional Power ◽  
Learning Control ◽  
Convergence Condition ◽  
Iterative Learning ◽  
Reference Trajectory ◽  
Order Tracking ◽  
Consensus Tracking

This paper deals with the consensus tracking problem of heterogeneous linear multiagent systems under the repeatable operation environment, and adopts a proportional differential (PD)-type iterative learning control (ILC) algorithm based on the fractional-power tracking error. According to graph theory and operator theory, convergence condition is obtained for the systems under the interconnection topology that contains a spanning tree rooted at the reference trajectory named as the leader. Our algorithm based on fractional-power tracking error achieves a faster convergence rate than the usual PD-type ILC algorithm based on the integer-order tracking error. Simulation examples illustrate the correctness of our proposed algorithm.

Iterative Learning Control With Data-Driven-Based Compensation

2021 ◽  
pp. 1-12
Author(s):  
Shaoying He ◽  
Wenbo Chen ◽  
Dewei Li ◽  
Yugeng Xi ◽  
Yunwen Xu ◽  
...  
Keyword(s):  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Data Driven

Observer-based data-driven iterative learning control

2020 ◽  
Vol 51 (13) ◽  
pp. 2343-2359
Author(s):  
Ronghu Chi ◽  
Yangchun Wei ◽  
Wenlong Yao ◽  
Jianmin Xing
Keyword(s):  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Data Driven

Data-driven optimal terminal iterative learning control with initial value dynamic compensation

2016 ◽  
Vol 10 (12) ◽  
pp. 1357-1364 ◽  
Author(s):  
Ronghu Chi ◽  
Ruikun Zhang ◽  
Yuanjing Feng ◽  
Biao Huang ◽  
Danwei Wang
Keyword(s):  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Data Driven ◽  
Initial Value ◽  
Dynamic Compensation
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