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Prescribed performance adaptive DSC for a class of time-delayed switched nonlinear systems in nonstrict-feedback form: Application to a two-stage chemical reactor
Journal of Process Control ( IF 4.2 ) Pub Date : 2020-05-01 , DOI: 10.1016/j.jprocont.2020.03.010
Seyyed Mostafa Tabatabaei , Sara Kamali , Mohammad Mehdi Arefi , Jinde Cao

Abstract This study deals with the tracking problem for a class of nonstrict-feedback switched nonlinear systems (SNSs) with unknown time-delay and unknown functions under arbitrary switching. To achieve this goal, an adaptive neural network-based dynamic surface control (DSC) based on backstepping approach is proposed. A neural network (NN) approximator based on radial basis functions (RBFs) is utilized to approximate unknown functions. Considering properties of Gaussian basis function in RBFNNs, an adaptive neural network DSC for nonstrict-feedback structure has been developed. A Lyapunov-krasovskii functional is applied to compensate the effect of unknown delay terms. Furthermore, a prescribed performance bound (PPB) control strategy is utilized to retain the tracking error within a predefined bound. Finally, a practical example is provided to prove the effectiveness of the proposed method.

中文翻译:

一类非严格反馈形式的时滞切换非线性系统的规定性能自适应DSC:在两级化学反应器中的应用

摘要 本研究涉及一类时滞未知、函数未知的任意切换下的非严格反馈切换非线性系统(SNSs)的跟踪问题。为了实现这一目标,提出了一种基于反步法的基于自适应神经网络的动态表面控制(DSC)。基于径向基函数 (RBF) 的神经网络 (NN) 逼近器用于逼近未知函数。考虑到 RBFNN 中高斯基函数的特性,开发了一种用于非严格反馈结构的自适应神经网络 DSC。应用 Lyapunov-krasovskii 泛函来补偿未知延迟项的影响。此外,使用规定的性能界限 (PPB) 控制策略将跟踪误差保持在预定义的界限内。最后,
更新日期:2020-05-01
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