This work investigates the stabilization problem of uncertain stochastic Markovian jump systems (MJSs) under communication constraints. To reduce the bandwidth usage, a discrete-time Markovian chain is employed to implement the stochastic communication protocol (SCP) scheduling of the sensor nodes, by which only one sensor node is chosen to access the network at each transmission instant. Moreover, due to the effect of amplitude attenuation, time delay, and random interference/noise, the transmission may be inevitably subject to the Rice fading phenomenon. All of these constraints make the controller only receive the fading signal from one activated sensor node at each instant. A merge approach is first used to deal with two Markovian chains; meanwhile, a compensator is designed to provide available information for the controller. By a compensator and mode-based sliding-mode controller, the resulting closed-loop system is ensured to be input-to-state stable in probability (ISSiP), and the quasisliding mode is attained. Moreover, an iteration optimizing algorithm is provided to reduce the convergence domain around the sliding surface via searching a desirable sliding gain, which constitutes an effective GA-based sliding-mode control strategy. Finally, the proposed control scheme is verified via the simulation results.

中文翻译：

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通信约束下随机马尔可夫跳跃系统的输入状态稳定：基于遗传算法的性能优化。

这项工作研究了在通信约束下不确定随机Markovian跳跃系统（MJSs）的镇定问题。为了减少带宽使用，采用离散时间马尔可夫链来实现传感器节点的随机通信协议（SCP）调度，通过该调度，仅选择一个传感器节点在每个传输时刻访问网络。此外，由于幅度衰减，时间延迟和随机干扰/噪声的影响，传输中不可避免地会发生莱斯衰落现象。所有这些限制使得控制器在每个时刻仅从一个激活的传感器节点接收衰落信号。首先使用合并方法来处理两个马尔可夫链。同时，设计了补偿器以为控制器提供可用信息。通过补偿器和基于模式的滑模控制器，确保了所得的闭环系统输入到状态的概率稳定（ISSiP），并获得了准滑模。此外，提供了一种迭代优化算法，以通过搜索所需的滑动增益来减小滑动表面周围的收敛域，从而构成了一种有效的基于GA的滑模控制策略。最后，通过仿真结果验证了所提出的控制方案。构成了一种有效的基于GA的滑模控制策略。最后，通过仿真结果验证了所提出的控制方案。构成了一种有效的基于GA的滑模控制策略。最后，通过仿真结果验证了所提出的控制方案。