Data-Driven Formation Control for Unknown MIMO Nonlinear Discrete-Time Multi-Agent Systems With Sensor Fault

A data-driven distributed formation control algorithm is proposed for an unknown heterogeneous non-affine nonlinear discrete-time MIMO multi-agent system (MAS) with sensor fault. For the considered unknown MAS, the dynamic linearization technique in model-free adaptive control (MFAC) theory is used...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems Vol. 33; no. 12; pp. 7728 - 7742
Main Authors: Xiong, Shuangshuang, Hou, Zhongsheng
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.12.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Actuators
Adaptation models
Adaptive control
Algorithms
Control algorithms
Control theory
Data models
Discrete time systems
Error analysis
Field-flow fractionation
Formation control
Linearization
Mathematical model
MIMO (control systems)
MIMO communication
model free adaptive control (MFAC)
multi-agent system (MAS)
Multiagent systems
Neural networks
Radial basis function
radial basis function neural network (RBFNN)
sensor fault
Sensors
Stability analysis
ISSN:2162-237X, 2162-2388, 2162-2388
Online Access:Get full text
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Summary:A data-driven distributed formation control algorithm is proposed for an unknown heterogeneous non-affine nonlinear discrete-time MIMO multi-agent system (MAS) with sensor fault. For the considered unknown MAS, the dynamic linearization technique in model-free adaptive control (MFAC) theory is used to transform the unknown MAS into an equivalent virtual dynamic linearization data model. Then using the virtual data model, the structure of the distributed model-free adaptive controller is constructed. For the incorrect signal measurements due to the sensor fault, the radial basis function neural network (RBFNN) is first trained for the MAS under the fault-free case, then using the outputs of the well-trained RBFNN and the actual outputs of MAS under sensor fault case, the estimation laws of the unknown fault and system parameters in the virtual data model are designed with only the measured input-output (I/O) data information. Finally, the boundedness of the formation error is analyzed by the contraction mapping method and mathematical induction method. The effectiveness of the proposed algorithm is illustrated by simulation examples.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2021.3087481