Model-Based Networked Control of Spatially-Distributed Processes with Event-Triggered Parameter Re-identification

This work presents a methodology for the integration of time-triggered model-based state-feedback control and event-driven model re-identification for spatially-distributed processes modeled by highly-dissipative PDEs controlled over resource-limited communication networks. The methodology aims to enhance the closed-loop stability and performance properties of the networked closed-loop system in the presence of process parameter variations and external disturbances, while simultaneously reducing the rate of sensor-controller information transfer required. This is achieved by first designing a networked feedback controller on the basis of an approximate finite-dimensional model that captures the dominant dynamics of the infinite-dimensional system, and then developing an error monitoring scheme with a time-varying instability alarm threshold to track the state evolution and trigger model reidentification and model parameter updates in the event of parametric drift. When the alarm threshold is breached, a safe-parking protocol is initiated to counter the destabilizing influence of the drift and allow the identification of a new finite-dimensional model using the input and state data collected. The stability of the new model is then analyzed to determine the feasible post-drift sensor-controller communication rate. The development and implementation of the proposed methodology are demonstrated using a representative diffusion-reaction process example.