期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷 -, 期 -, 页码 -出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2023.3262799
关键词
Convergence; Stochastic processes; Sensitivity; Multi-agent systems; Stochastic systems; Process control; Lyapunov methods; Cooperative control; deferred state constraints; preassigned time control; stochastic multiagent system (MAS)
This article proposes a preassigned time adaptive tracking control problem for stochastic multiagent systems with deferred full state constraints and deferred prescribed performance. A modified nonlinear mapping is designed to eliminate the constraints on the initial value conditions and achieve deferred prescribed performance for stochastic MASs that provide only local information.
This article studies a preassigned time adaptive tracking control problem for stochastic multiagent systems (MASs) with deferred full state constraints and deferred prescribed performance. A modified nonlinear mapping is designed, which incorporates a class of shift functions, to eliminate the constraints on the initial value conditions. By virtue of this nonlinear mapping, the feasibility conditions of the full state constraints for stochastic MASs can also be circumvented. In addition, the Lyapunov function codesigned by the shift function and the fixed-time prescribed performance function is constructed. The unknown nonlinear terms of the converted systems are handled based on the approximation property of the neural networks. Furthermore, a preassigned time adaptive tracking controller is established, which can achieve deferred prescribed performance for stochastic MASs that provide only local information. Finally, a numerical example is given to demonstrate the effectiveness of the proposed scheme.
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