Abstract
This paper investigates the observer-based adaptive finite-time neural control issue of stochastic non-strict-feedback nonlinear systems. By establishing a state observer and utilizing the approximation property of the neural network, an adaptive neural network output-feedback controller is constructed. The controller solves the issue that the states of stochastic nonlinear system cannot be measured, and assures that all signals in the closed-loop system are bounded. Different from the existing adaptive control researches of stochastic nonlinear systems with unmeasured states, the proposed control scheme can guarantee the finite-time stability of the stochastic nonlinear systems. Furthermore, the effectiveness of the proposed control approach is verified by the simulation results.
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Yan Zhang received her B.S. degree from the Luliang University, Luliang, China, in 2018. She is currently pursuing an M.S. degree with the College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao, China. Her research interests include neural network control, backstepping control, finite-time control, and adaptive control.
Fang Wang received her B.S. degree from the Qufu Normal University, Qufu, China, an M.S. degree from Shandong Normal University, Jinan, China, and a Ph.D. degree from Guangdong University of Technology, Guangzhou, China, in 1997, 2004, and 2015, respectively. Since 2005, she has been at the Shandong University of Science and Technology, Qingdao, China. Her current research interests include stochastic nonlinear control systems, multi-agent systems, quantized control, and adaptive fuzzy control.
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Zhang, Y., Wang, F. Observer-based Finite-time Control of Stochastic Non-strict-feedback Nonlinear Systems. Int. J. Control Autom. Syst. 19, 655–665 (2021). https://doi.org/10.1007/s12555-019-0951-2
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DOI: https://doi.org/10.1007/s12555-019-0951-2