Stabilization of uncertain systems using quantized and lossy observations and uncertain control inputs
For control engineers designing networked control systems with parameter uncertainty, this provides theoretical limits on data rate and packet loss for stability.
The paper derives conditions for mean square stabilization of uncertain autoregressive systems under quantized and lossy observations, showing that nonuniform quantizers achieve stability with lower data rate than uniform ones.
In this paper, we consider a stabilization problem of an uncertain system in a networked control setting. Due to the network, the measurements are quantized to finite-bit signals and may be randomly lost in the communication. We study uncertain autoregressive systems whose state and input parameters vary within given intervals. We derive conditions for making the plant output to be mean square stable, characterizing limitations on data rate, packet loss probabilities, and magnitudes of uncertainty. It is shown that a specific class of nonuniform quantizers can achieve stability with a lower data rate compared with the common uniform one.