Neural-Based Adaptive Output-Feedback Control for a Class of Nonstrict-Feedback Stochastic Nonlinear Systems
In this paper, we consider the problem of observer-based adaptive neural output-feedback control for a class of stochastic nonlinear systems with nonstrict-feedback structure. To overcome the design difficulty from the nonstrict-feedback structure, a variable separation approach is introduced by usi...
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Veröffentlicht in: | IEEE transactions on cybernetics 2015-09, Vol.45 (9), p.1977-1987 |
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container_end_page | 1987 |
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container_issue | 9 |
container_start_page | 1977 |
container_title | IEEE transactions on cybernetics |
container_volume | 45 |
creator | Wang, Huanqing Liu, Kefu Liu, Xiaoping Chen, Bing Lin, Chong |
description | In this paper, we consider the problem of observer-based adaptive neural output-feedback control for a class of stochastic nonlinear systems with nonstrict-feedback structure. To overcome the design difficulty from the nonstrict-feedback structure, a variable separation approach is introduced by using the monotonically increasing property of system bounding functions. On the basis of the state observer, and by combining the adaptive backstepping technique with radial basis function neural networks' universal approximation capability, an adaptive neural output feedback control algorithm is presented. It is shown that the proposed controller can guarantee that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded in the sense of mean quartic value. Simulation results are provided to show the effectiveness of the proposed control scheme. |
doi_str_mv | 10.1109/TCYB.2014.2363073 |
format | Article |
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To overcome the design difficulty from the nonstrict-feedback structure, a variable separation approach is introduced by using the monotonically increasing property of system bounding functions. On the basis of the state observer, and by combining the adaptive backstepping technique with radial basis function neural networks' universal approximation capability, an adaptive neural output feedback control algorithm is presented. It is shown that the proposed controller can guarantee that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded in the sense of mean quartic value. Simulation results are provided to show the effectiveness of the proposed control scheme.</abstract><cop>United States</cop><pub>IEEE</pub><pmid>25361516</pmid><doi>10.1109/TCYB.2014.2363073</doi><tpages>11</tpages></addata></record> |
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subjects | Adaptive neural output-feedback control Adaptive systems Approximation methods Backstepping Computer Simulation Feedback Neural Networks (Computer) Nonlinear Dynamics Nonlinear systems nonstrict-feedback structure Observers Output feedback stochastic nonlinear systems Stochastic Processes |
title | Neural-Based Adaptive Output-Feedback Control for a Class of Nonstrict-Feedback Stochastic Nonlinear Systems |
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