论文标题
最佳控制器的合成和线性界面高斯系统的动态量化器切换
Optimal Controller Synthesis and Dynamic Quantizer Switching for Linear-Quadratic-Gaussian Systems
论文作者
论文摘要
在网络控制系统中，通常在传输到控制器之前对感觉信号进行量化。因此，性能受到此量化过程的粗糙度的影响。现代通信技术允许用户根据所支付的价格获得量化的量化测量。在本文中，我们考虑了量化的反馈线性 - 季度高斯（QF-LQG）系统的最佳控制器的合成，其中要在传输到控制器之前对测量值进行量化。该系统提供了几种量化器的选择，以及操作每个量化器的成本。目的是共同选择将在控制性能和量化成本之间保持最佳平衡的量化器和控制器。在某些假设下，可以将此问题分解为两个优化问题：一个用于最佳控制器合成，另一个用于最佳量化器选择。我们表明，与经典的LQG问题类似，最佳控制器综合子问题的特征是Riccati方程。另一方面，通过求解某个Markov-Decision-Process（MDP），可以找到最佳的量化器选择策略。
In networked control systems, often the sensory signals are quantized before being transmitted to the controller. Consequently, performance is affected by the coarseness of this quantization process. Modern communication technologies allow users to obtain resolution-varying quantized measurements based on the prices paid. In this paper, we consider optimal controller synthesis of a Quantized-Feedback Linear-Quadratic-Gaussian (QF-LQG) system where the measurements are to be quantized before being transmitted to the controller. The system is presented with several choices of quantizers, along with the cost of operating each quantizer. The objective is to jointly select the quantizers and the controller that would maintain an optimal balance between control performance and quantization cost. Under certain assumptions, this problem can be decoupled into two optimization problems: one for optimal controller synthesis and the other for optimal quantizer selection. We show that, similarly to the classical LQG problem, the optimal controller synthesis subproblem is characterized by Riccati equations. On the other hand, the optimal quantizer selection policy is found by solving a certain Markov-Decision-Process (MDP).
