Adaptive Output-Feedback Neural Control of Switched Uncertain Nonlinear Systems With Average Dwell Time
Adaptive Output-Feedback Neural Control of Switched Uncertain Nonlinear Systems With Average Dwell Time
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DOI：
10.1109/tnnls.2014.2341242
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发表时间：
2015-07
影响因子：
10.4
通讯作者：
Lijun Long;Jun Zhao
中科院分区：
文献类型：
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作者：
Lijun Long;Jun Zhao
研究了一类不确定非线性切换系统的输出反馈自适应神经跟踪控制问题。未知控制信号直接由神经网络逼近。针对所研究的问题，利用平均停留时间法和反推法建立了一种新的自适应神经网络控制方法。设计了一个切换滤波器和不同的更新律，以减小由于所有子系统采用公共观测器和公共更新律而引起的保守性。所设计的子系统控制器保证了在一类具有平均驻留时间的切换信号下，所有闭环信号保持有界，同时输出跟踪误差收敛到原点的一个小邻域内.作为所提出设计方法的应用，构造了质量-弹簧-阻尼系统的自适应输出反馈神经跟踪控制器。
This paper investigates the problem of adaptive neural tracking control via output-feedback for a class of switched uncertain nonlinear systems without the measurements of the system states. The unknown control signals are approximated directly by neural networks. A novel adaptive neural control technique for the problem studied is set up by exploiting the average dwell time method and backstepping. A switched filter and different update laws are designed to reduce the conservativeness caused by adoption of a common observer and a common update law for all subsystems. The proposed controllers of subsystems guarantee that all closed-loop signals remain bounded under a class of switching signals with average dwell time, while the output tracking error converges to a small neighborhood of the origin. As an application of the proposed design method, adaptive output feedback neural tracking controllers for a mass-spring-damper system are constructed.