Data-Driven Distributed Predictive Tracking Control for Heterogeneous Nonlinear Multiagent Systems With Communication Delays

This article focuses on the consensus and tracking problem of nonlinear multiagent systems under communication delays, and proposes a distributed predictive control scheme, which is independent of the system model and able to actively compensate for communication delays. First, a consensus-related a...

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Bibliographic Details
Published in:IEEE transactions on automatic control Vol. 69; no. 7; pp. 4786 - 4792
Main Authors: Huang, Yi, Liu, Guo-Ping, Yu, Yi, Hu, Wenshan
Format: Journal Article
Language:English
Published: New York IEEE 01.07.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Closed loops
Communication
Communication delays
Communication networks
Control methods
Controllers
data-driven control
Delays
distributed predictive control
Feedback control
Linear systems
Multi-agent systems
Multiagent systems
networked control
Nonlinear control
Nonlinear dynamical systems
Nonlinear dynamics
nonlinear multiagent systems
Nonlinear systems
Predictive control
Predictive models
Stability analysis
Tracking control
Tracking problem
Vehicle dynamics
ISSN:0018-9286, 1558-2523
Online Access:Get full text
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Summary:This article focuses on the consensus and tracking problem of nonlinear multiagent systems under communication delays, and proposes a distributed predictive control scheme, which is independent of the system model and able to actively compensate for communication delays. First, a consensus-related auxiliary variable and its corresponding estimator are innovatively designed to provide a globally consistent value. Then, the idea of dynamic linearization is adopted to obtain the equivalent linear dynamics of nonlinear agents. Furthermore, a distributed predictive controller is designed with the help of the auxiliary variables and the acquired time-varying linear system model. Compared with the existing schemes, the proposed controller compensates for the delayed signals in an active way, makes the traditional model-based predictive control method get rid of the dependence on the accurate system model, and carries out the rolling prediction in a distributed form. A stability analysis of the closed-loop system is given to illustrate the generality of the designed method. Finally, a numerical simulation verifies the effectiveness of the proposed method.
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ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2024.3357529