Jointly Optimal Quantization, Estimation, and Control of Hidden Markov Chains

It is of interest to understand the tradeoff between the communication resource consumption and the achievable system performance in networked control systems. In this paper we explore a general framework for tradeoff analysis and decision making in such systems by studying joint quantization, estimation, and control of a hidden Markov chain. Dynamic programming is used to find the optimal quantization and control scheme that minimizes a weighted combination of different cost terms including the communication cost, the delay, the estimation error, and the running cost. Simulation and analysis based on example problems show that this approach is able to capture the tradeoffs among competing objectives by adjusting the cost weights.