Network Estimation and Packet Delivery Prediction for Control over Wireless Mesh Networks
For control systems operating over wireless mesh networks, this work provides a method to predict packet delivery sequences, allowing controllers to compensate for predicted outages, which is an incremental improvement over existing point-to-point models.
This work models a wireless mesh network as a graph of links with transmission success probabilities and uses a recursive Bayesian estimator to provide packet delivery predictions to the controller, enabling improved LQG control over lossy actuation channels.
Much of the current theory of networked control systems uses simple point-to-point communication models as an abstraction of the underlying network. As a result, the controller has very limited information on the network conditions and performs suboptimally. This work models the underlying wireless multihop mesh network as a graph of links with transmission success probabilities, and uses a recursive Bayesian estimator to provide packet delivery predictions to the controller. The predictions are a joint probability distribution on future packet delivery sequences, and thus capture correlations between successive packet deliveries. We look at finite horizon LQG control over a lossy actuation channel and a perfect sensing channel, both without delay, to study how the controller can compensate for predicted network outages.