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Distributed Adaptive Output Feedback Consensus for Nonlinear Stochastic Multiagent Systems by Reference Generator Approach
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DOI：10.1109/TNNLS.2023.3253080.png)
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This article investigates the distributed leader-following consensus for a class of nonlinear stochastic multiagent systems (MASs) under directed communication topology. In order to estimate unmeasured system states, a dynamic gain filter is designed for each control input with reduced filtering variables. Then, a novel reference generator is proposed, which plays a key role in relaxing the restriction on communication topology. Based on the reference generators and filters, a distributed output feedback consensus protocol is proposed by a recursive control design approach, which incorporates adaptive radial basis function (RBF) neural networks to approximate the unknown parameters and functions. Compared with existing works on stochastic MASs, the proposed approach can significantly reduce the number of dynamic variables in filters. Furthermore, the agents considered in this article are quite general with multiple uncertain/unmatched inputs and stochastic disturbance. Finally, a simulation example is given to demonstrate the effectiveness of our results.
Keyword:
Stochastic processes
Control systems
Nonlinear dynamical systems
Generators
Consensus protocol
Output feedback
Neural networks
Adaptive radial basis function (RBF) neural networks
distributed reference generator
dynamic gain filters
leader-following consensus
nonlinear stochastic multiagent systems (MASs)
期刊
IF:
8.9
论文数:
7.6K
被引数:
7.2W
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