摘要
This paper focuses on investigating the issue of adaptive state-feedback control based on neural networks(NNs)for a class of high-order stochastic uncertain systems with unknown nonlinearities. By introducing the radial basis function neural network(RBFNN) approximation method, utilizing the backstepping method and choosing an approximate Lyapunov function, we construct an adaptive state-feedback controller which assures the closed-loop system to be mean square semi-global-uniformly ultimately bounded(M-SGUUB). A simulation example is shown to illustrate the effectiveness of the design scheme.
This paper focuses on investigating the issue of adaptive state-feedback control based on neural networks（NNs）for a class of high-order stochastic uncertain systems with unknown nonlinearities. By introducing the radial basis function neural network（RBFNN） approximation method, utilizing the backstepping method and choosing an approximate Lyapunov function, we construct an adaptive state-feedback controller which assures the closed-loop system to be mean square semi-global-uniformly ultimately bounded（M-SGUUB）. A simulation example is shown to illustrate the effectiveness of the design scheme.
出处
《自动化学报》
EI
CSCD
北大核心
2014年第12期2968-2972,共5页
Acta Automatica Sinica
基金
Supported by National Natural Science Foundation of China(61104222,61305149)
Natural Science Foundation of JiangsuProvince(BK2011205)
333 High-Level Talents Training Program inJiangsu Province
Program for Fundamental Research of Natural Sciences in Universities of Jiangsu Province(11KJB510026)
Natural Science Foundation of Jiangsu Normal University(11XLR08)
