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Network-Based Filtering for Stochastic Markovian Jump Systems with Application to PWM-Driven Boost Converter

机译:随机马尔可夫跳跃系统的基于网络的滤波及其在PWM驱动的Boost转换器中的应用

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This paper investigates the filtering problem for a class of stochastic jump systems over network communication channel. The plant under consideration is a class of Markovian jump systems with state-dependent noise. The network communication links between the plant and filter are impact, and the effects of network-induced transmission delay and sensor saturation are taken into simultaneous consideration. The main difficulty in this filtering problem is that there exists transmission delay in the received mode signals of the filter side, which results in that the real-time information of jump mode is not accessible. To overcome this obstacle, in this paper, an state space augmentation approach is developed for the jumping mode r(k), based on which the resulting filtering dynamics is modeled as a new Markovian jump system with two jumping parameters. A mode-dependent filtering scheme is then developed to guarantee that the resulting overall system is stochastically stable with a guaranteed performance index. Finally, a numerical example of pulse-width-modulation-driven boost converter is included to show the effectiveness of the networked filtering design strategy.
机译:本文研究了一类基于网络通信信道的随机跳跃系统的过滤问题。所考虑的设备是一类具有状态相关噪声的马尔可夫跳跃系统。设备和过滤器之间的网络通信链接受到影响,同时考虑了网络引起的传输延迟和传感器饱和的影响。该滤波问题的主要困难在于,在滤波器侧的接收模式信号中存在传输延迟,这导致不能访问跳转模式的实时信息。为了克服这一障碍,本文针对跳跃模式r(k)开发了一种状态空间增强方法,在此基础上,将所得的滤波动力学建模为具有两个跳跃参数的新马尔科夫跳跃系统。然后开发一种与模式有关的过滤方案,以确保所得的整个系统在随机的情况下保持稳定,并保证了性能指标。最后,给出了一个由脉宽调制驱动的升压转换器的数值示例,以说明网络滤波设计策略的有效性。

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