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Adaptive neural network control for stochastic constrained block structure nonlinear systems with dynamical uncertainties
Author(s) -
Xia Meizhen,
Zhang Tianping
Publication year - 2019
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.3010
Subject(s) - control theory (sociology) , nonlinear system , mimo , bounded function , controller (irrigation) , artificial neural network , adaptive control , moment (physics) , computer science , mathematics , control (management) , artificial intelligence , computer network , mathematical analysis , channel (broadcasting) , physics , classical mechanics , quantum mechanics , agronomy , biology
Summary Stochastic adaptive dynamic surface control is presented for a class of uncertain multiple‐input–multiple‐output (MIMO) nonlinear systems with unmodeled dynamics and full state constraints in this paper. The controller is constructed by combining the dynamic surface control with radial basis function neural networks for the MIMO stochastic nonlinear systems. The nonlinear mapping is applied to guarantee the state constraints being not violated. The unmodeled dynamics is disposed through introducing an available dynamic signal. It is proved that all signals in the closed‐loop system are bounded in probability and the error signals are semiglobally uniformly ultimately bounded in mean square or the sense of four‐moment and the state constraints are confirmed in probability. Simulation results are offered to further illustrate the effectiveness of the control scheme.