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Distributed Adaptive Forwarding Finite-Time Output Consensus of High-Order Multiagent Systems via Immersion and Invariance-Based Approximator.
- Source :
-
IEEE transactions on neural networks and learning systems [IEEE Trans Neural Netw Learn Syst] 2024 Apr; Vol. 35 (4), pp. 5241-5255. Date of Electronic Publication: 2024 Apr 04. - Publication Year :
- 2024
-
Abstract
- A finite-time output consensus control problem is investigated in this article for an uncertain nonlinear high-order multiagent systems (MASs). For this class of MASs, the order of individual follower is reduced gradually by implementing the immersion and invariance (I&I) control theory repeatedly, and a requirement of solving partial differential equations (PDEs) in I&I control theory is obviated. Furthermore, an I&I-based radial basis function neural network (RBFNN) approximator is developed, where an extra cross term is added in the approximation mechanism, and the form of an update law for weights is transformed into a proportional and integral one. This I&I-based RBFNN approximator does not rely on a cancellation of the perturbation term, and these uncertainties are reconstructed by the I&I manifold adaptively, which is for improvement of approximation behaviors of traditional RBFNNs. On this basis, a distributed adaptive forwarding finite-time output consensus control strategy is proposed by combining a sign function, and the convergence time of the MAS can be adjusted with appropriate finite-time parameters. Finally, two illustrative examples verify the effectiveness of the theoretical claims.
Details
- Language :
- English
- ISSN :
- 2162-2388
- Volume :
- 35
- Issue :
- 4
- Database :
- MEDLINE
- Journal :
- IEEE transactions on neural networks and learning systems
- Publication Type :
- Academic Journal
- Accession number :
- 36121956
- Full Text :
- https://doi.org/10.1109/TNNLS.2022.3203011