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Decentralized adaptive fuzzy control for nonlinear large-scale systems with random packet dropouts, sensor delays and nonlinearities

机译：具有随机分组丢失，传感器延迟和非线性的非线性大规模系统的分散自适应模糊控制

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This paper investigates the adaptive fuzzy decentralized control problem for a class of uncertain nonlinear large-scale systems. Different from the conventional backstepping-based adaptive output feedback control technique, the output measurements are imperfect and considered to suffer random packet dropouts (RPDs), random sensor delays (RSDs) and random sensor nonlinearities (RSNs), which result typically from a network environment such as sensor networks. A novel sensor model is established for describing the random phenomena within a unified representation by introducing a multi-Markovian variable. Based on this sensor model, two main difficulties arise: first is that the system output cannot be used directly for controller design due to the randomly occurring phenomena; second is how to deal with the complex nonlinear stochastic terms with unknown interconnections, unknown time-varying delays and unknown sensor nonlinearities entangled together. With the help of new coordinate transformations and mean value theorem, by using appropriate Lyapunov-Krasovskii functionals, a new adaptive decentralized memoryless output feedback controller is designed. It is proved that the constructed controller ensures the boundedness in probability of all the closed-loop signals in presence of RPDs, RSDs and RSNs. The simulation results are presented to show the effectiveness of the proposed scheme. (C) 2017 Elsevier B.V. All rights reserved.
机译：研究了一类不确定的非线性大系统的自适应模糊分散控制问题。与传统的基于Backstepping的自适应输出反馈控制技术不同，输出测量是不完美的，并且会受到随机分组丢失（RPD），随机传感器延迟（RSD）和随机传感器非线性（RSN）的影响，这通常是由网络环境造成的例如传感器网络。建立了一种新颖的传感器模型，通过引入多马尔可夫变量来描述统一表示内的随机现象。基于这种传感器模型，出现了两个主要困难：首先，由于随机发生的现象，系统输出不能直接用于控制器设计；其次是如何处理未知互连，未知时变延迟和未知传感器非线性纠缠在一起的复杂非线性随机项。借助于新的坐标变换和均值定理，通过使用适当的Lyapunov-Krasovskii函数，设计了一种新的自适应分散式无记忆输出反馈控制器。证明了所构造的控制器在存在RPD，RSD和RSN的情况下确保了所有闭环信号的概率有界。仿真结果表明了该方案的有效性。 （C）2017 Elsevier B.V.保留所有权利。

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