Optimal Controller and Quantizer Selection for Partially Observable Linear-Quadratic-Gaussian Systems

TL;DR

This paper develops a method for jointly optimizing controller design and quantizer selection in partially observable LQG systems, balancing control performance and quantization costs using offline solvable optimization problems.

Contribution

It introduces a joint optimization framework for controller synthesis and quantizer scheduling in quantized feedback systems, with solutions based on Riccati equations and linear programming.

Findings

Optimal controller derived via Riccati equation

Quantizer selection optimized through linear programming

Framework applicable to systems with quantized measurements

Abstract

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 joint optimal controller synthesis and quantizer scheduling for a partially observed Quantized-Feedback Linear-Quadratic-Gaussian (QF-LQG) system, where the measurements are quantized before being sent to the controller. The system is presented with several choices of quantizers, along with the cost of using each quantizer. The objective is to jointly select the quantizers and synthesize the controller to strike an optimal balance between control performance and quantization cost. When the innovation signal is quantized instead of the measurement, the problem is decoupled into two optimization problems: one for optimal controller synthesis, and the other for optimal quantizer selection. The optimal controller is found by solving a Riccati equation and the optimal quantizer selection policy is found by solving a linear program (LP)- both of which can be solved offline.