In this work, a minimum variance estimator is designed for a networked system with inherent network imperfections in both sensor to estimator (S‐E) and controller to actuator (C‐A) channels simultaneously. The channels are affected by packet delays, dropouts, and uncertain observations. These effects are modeled using five Bernoulli distributed random variables. Correlation of noise at neighboring time caused by random delay is avoided by introducing two additional variables in the augmented stochastic model. The developed augmented stochastic model can handle network imperfections in both the S‐E and C‐A channels simultaneously. A minimum variance recursive linear estimator is designed using an innovation approach and projection theorem. Furthermore, sufficient condition is presented for the existence of steady state property of the proposed estimator. Simulation studies are carried out for the proposed estimator using a numerical example and a single link robot arm. Finally, performance comparison with other popular filters shows the effectiveness of the designed estimator.

中文翻译：

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具有S-E和C-A通道中的数据包延迟，数据包丢失和不确定观测的网络控制系统的状态估计

在这项工作中，为网络系统设计了最小方差估计器，该系统在传感器到估计器（S‐E）和控制器到执行器（CA）通道中同时具有固有的网络缺陷。信道受数据包延迟，丢失和不确定的观察结果的影响。使用五个伯努利分布随机变量对这些影响进行建模。通过在增强型随机模型中引入两个其他变量，可以避免由随机延迟导致的相邻时间噪声的相关性。开发的增强型随机模型可以同时处理S-E和C-A通道中的网络缺陷。使用创新方法和投影定理设计最小方差递归线性估计量。此外，给出了估计器存在稳态特性的充分条件。使用数值示例和单链接机器人手臂对拟议的估算器进行了仿真研究。最后，与其他流行滤波器的性能比较显示了设计的估算器的有效性。