IEEE Trans Neural Netw Learn Syst - Output-feedback adaptive neural control for stochastic nonlinear time-varying delay systems with unknown control directions.

Tópicos

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Resumo

This paper presents an adaptive output-feedback neural network (NN) control scheme for a class of stochastic nonlinear time-varying delay systems with unknown control directions. To make the controller design feasible, the unknown control coefficients are grouped together and the original system is transformed into a new system using a linear state transformation technique. Then, the Nussbaum function technique is incorporated into the backstepping recursive design technique to solve the problem of unknown control directions. Furthermore, under the assumption that the time-varying delays exist in the system output, only one NN is employed to compensate for all unknown nonlinear terms depending on the delayed output. Moreover, by estimating the maximum of NN parameters instead of the parameters themselves, the NN parameters to be estimated are greatly decreased and the online learning time is also dramatically decreased. It is shown that all the signals of the closed-loop system are bounded in probability. The effectiveness of the proposed scheme is demonstrated by the simulation results.

Resumo Limpo

paper present adapt outputfeedback neural network nn control scheme class stochast nonlinear timevari delay system unknown control direct make control design feasibl unknown control coeffici group togeth origin system transform new system use linear state transform techniqu nussbaum function techniqu incorpor backstep recurs design techniqu solv problem unknown control direct furthermor assumpt timevari delay exist system output one nn employ compens unknown nonlinear term depend delay output moreov estim maximum nn paramet instead paramet nn paramet estim great decreas onlin learn time also dramat decreas shown signal closedloop system bound probabl effect propos scheme demonstr simul result

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