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Stabilization of networked control systems via non-monotone control Lyapunov functions

机译:通过非单调控制Lyapunov功能稳定网络控制系统

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摘要

This paper deals with stabilization of networked control systems (NCS) affected by uncertain time-varying delays and data packet dropouts. We point out that such network effects are likely to render the classical control Lyapunov function (CLF) method unfeasible, mainly due to the monotonic decreasing condition. To solve this problem we make use of a discrete-time equivalent of a control Lyapunov-Razumikhin function (CLRF), which is allowed to be non-monotone. The corresponding stabilizing control law is obtained by solving an optimization problem on-line, in a receding horizon manner, which incorporates the available knowledge about the past state/input trajectory. Furthermore, we provide extra flexibility to the CLRF via a relaxation variable, which is needed to handle hard state/input constraints. We also propose an effective method for dealing with delays larger than the sampling period by means of an on-line Minkowski set addition. This makes it possible to guarantee stability even in the presence of data packet dropouts, under certain assumptions.
机译:本文讨论了网络控制系统(NCS)的稳定性,该系统受不确定的时变延迟和数据包丢失的影响。我们指出,这种网络效应很可能使经典控制Lyapunov函数(CLF)方法不可行,这主要是由于单调递减条件造成的。为了解决此问题,我们使用了控制李雅普诺夫-拉祖米欣函数(CLRF)的离散时间等效项,该函数可以是非单调的。通过以后退的方式在线解决优化问题来获得相应的稳定控制律,该最优问题结合了关于过去状态/输入轨迹的可用知识。此外,我们通过松弛变量为CLRF提供了额外的灵活性,这是处理硬状态/输入约束所必需的。我们还提出了一种有效的方法,通过在线Minkowski集加法来处理大于采样周期的延迟。在某些假设下,即使在存在数据包丢失的情况下,这也可以保证稳定性。

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