Reduced communication state estimation for control of an unstable networked control system

A state estimation method is presented that allows the designer to trade off estimator performance for communication bandwidth in a networked control system. The method is based on a time-varying Kalman filter and a communication decision rule for each sensor: a sensor measurement is transmitted and used to update the Kalman filter if its associated prediction variance exceeds a certain tolerable bound. The resulting equation for the estimation error variance is deterministic, which enables its off-line analysis. If a periodic solution to the variance equation is found, it facilitates a straight-forward implementation of the communication decision: each sensor transmits its measurements with a fixed periodic sequence. This state estimation method is applied in the feedback control system of a cube balancing on one of its edges. Six rotating bodies on the cube stabilize the system and constitute the agents in the networked control system: each one is equipped with local actuation, sensing, and computation, and the agents share their sensor data over a broadcast network. Experimental results compare the performance of the reduced communication state estimation algorithm to a Kalman filter with full measurements.