IEEE Trans Cybern. 2019 Feb;49(2):638-648. doi: 10.1109/TCYB.2017.2784545. Epub 2018 Jan 3.
This paper focuses on the state estimator design problem for a class of Takagi-Sugeno fuzzy stochastic hybrid systems with intermittent measurements in discrete-time domain. The hybrid systems are characterized with the stochastic switching among a set of subsystems, and the switching is supposed to be governed by a semi-Markov process with finite sojourn time. The random packet dropouts are modeled by a Bernoulli distributed sequence, and the packet dropout rate, which is considered to be variable, is described by the semi-Markov stochastic process that governs the switching dynamics of the fuzzy stochastic hybrid system. A more general class of Lyapunov functions that not only depends on the system modes, but also on the time that the current mode has been in since the last mode switching, is employed to analyze the stability and H performance of the estimation error system. Then, numerically testable conditions on the existence of a desired fuzzy mode-dependent state estimator are presented such that the estimation error system approaches to be mean square stable to an adjustable level and achieves a prescribed H disturbance attenuation index. Finally, an illustrative example of a single-link robotic arm system is provided to demonstrate the effectiveness and the superiority of the design method of state estimator.
本文针对一类具有离散时间域间歇测量的 Takagi-Sugeno 模糊随机混合系统的状态估计器设计问题进行了研究。混合系统的特点是在一组子系统之间存在随机切换，并且切换被假设为由具有有限逗留时间的半马尔可夫过程控制。随机数据包丢失通过伯努利分布序列进行建模，并且考虑到可变的数据包丢失率，由控制模糊随机混合系统切换动态的半马尔可夫随机过程来描述。采用了一类更通用的 Lyapunov 函数，该函数不仅取决于系统模式，还取决于自上次模式切换以来当前模式已经运行的时间，用于分析估计误差系统的稳定性和 H 性能。然后，提出了存在期望模糊模式相关状态估计器的数值可检验条件，使得估计误差系统趋近于达到可调节水平的均方稳定，并实现规定的 H 干扰衰减指标。最后，通过单连杆机器人臂系统的实例说明了状态估计器设计方法的有效性和优越性。
