On networked control systems under time, measurement, and process uncertainties

Abstract

The performance of control loops closed over communication networks can deteriorate due to variable time delays caused by network congestion and latency and by the processing of the control algorithms, an effect known in periodic sampling as jitter. To remove this detrimental effect on stability/performance, the method of synchronization at actuation instants uses an observer to estimate the value of the states at actuation, knowing state measurements at sampling, which entails strictly periodic actuation. However, its successful application requires noise-free samples and undisturbed process issues are impossible. In this work, the model mentioned above is extended to the case of other non-ideal operating conditions. In particular, a Kalman filter is incorporated into the synchronized actuation model, considering that the available measurements of the states are not periodic. This raises the problem of adapting the standard discrete-time Kalman filter to the case under study and deciding when to apply the prediction and correction phases. The immediate benefit is that synchronized actuation eliminates the harmful effects of uncertainty over time, and the Kalman filter improves performance in the face of uncertainty in measurements and the process.