Robust neural output-feedback stabilization for stochastic nonlinear process with time-varying delay and unknown dead zone
Robust neural output-feedback stabilization for stochastic nonlinear process with time-varying delay and unknown dead zone作者机构：Department of Automation Shanghai Jiao Tong University Navigation College Dalian Maritime University
出 版 物：《Science China(Information Sciences)》 (中国科学:信息科学(英文版))
年 卷 期：2017年第60卷第12期
页 面：20-32页
核心收录：
学科分类：0711[理学-系统科学] 07[理学] 08[工学] 070105[理学-运筹学与控制论] 081101[工学-控制理论与控制工程] 071101[理学-系统理论] 0811[工学-控制科学与工程] 0701[理学-数学]
基 金：supported by National Postdoctoral Program for Innovative Talents(Grant No.BX201600103) China Postdoctoral Science Foundation(Grant No.2016M601600) National Natural Science Foundation of China(Grant Nos.61473183,U1509211) Fundamental Research Funds for the Central University(Grant No.3132016001)
主 题：stochastic system robust neural control time delay dead zone output-feedback
摘 要：This article investigates the output-feedback control of a class of stochastic nonlinear system with time-varying delay and unknown dead zone. A robust neural stabilizing algorithm is proposed by using the circle criterion, the NNs approximation and the MLP(minimum learning parameter) technique. In the scheme, the nonlinear observer is first designed to estimate the unmeasurable states and the assumption linear growth of the nonlinear function is released. Furthermore, the uncertainty of the whole system(including the perturbation of time-varying delay) is lumped and compensated by employing one RBF NNs(radial basis function neural networks). Though, only two weight-norm related parameters are required to be updated online for the merit of the MLP technique. And the gain-inversion related adaptive law is targetly designed to mitigate the adverse effect of unknown dead zone. Comparing with the previous work, the proposed algorithm obtains the advantage:a concise form and easy to implementation due to its less computational burden. The theoretical analysis and comparison example demonstrate the substantial effectiveness of the proposed scheme.
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