Optimal Controller Synthesis and Dynamic Quantizer Switching for Linear-Quadratic-Gaussian Systems

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 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).