具有通信延迟丢失的LQG系统鲁棒因果变换编码
Robust Causal Transform Coding for LQG Systems with Delay Loss in Communications
AI总结：
本文提出一种鲁棒因果变换编码方案，用于处理具有随机延迟丢失的网络控制系统，通过利用系统状态记忆实现高效通信并优化LQG闭环系统的控制器设计。
摘要：
本文考虑了一个在具有随机延迟丢失的信道上与控制器通信的网络控制系统。信道模型受到近期树码发展的影响，将擦除信道转换为具有随机延迟的信道。本文提出了一种因果变换编码方案，利用系统状态记忆实现高效通信（压缩）并提供对信道延迟丢失的鲁棒性。在此设置中，分析了线性二次高斯（LQG）闭环系统的性能及控制器设计。将LQG系统的变换码设计表述为一个信道优化的源编码问题，目标是通过信道最小化加权均方误差。解决方案分为两个步骤：获得优化的因果编码和解码变换以及在一组变换编码量化器之间分配速率。高斯-马尔可夫源和LQG系统的数值和仿真结果展示了所提方案的有效性。
英文摘要：
A networked controlled system (NCS) in which the plant communicates to the controller over a channel with random delay loss is considered. The channel model is motivated by recent development of tree codes for NCS, which effectively translates an erasure channel to one with random delay. A causal transform coding scheme is presented which exploits the plant state memory for efficient communications (compression) and provides robustness to channel delay loss. In this setting, we analyze the performance of linear quadratic Gaussian (LQG) closed-loop systems and the design of the optimal controller. The design of the transform code for LQG systems is posed as a channel optimized source coding problem of minimizing a weighted mean squared error over the channel. The solution is characterized in two steps of obtaining the optimized causal encoding and decoding transforms and rate allocation across a set of transform coding quantizers. Numerical and simulation results for Gauss-Markov sources and an LQG system demonstrate the effectiveness of the proposed schemes.
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