Simulation, state estimation and control of nonlinear superheater attemperator using neural networks

This paper considers the use of neural networks for nonlinear state estimation, system identification and control. As a case study we use data taken from a nonlinear injection valve for a superheater attemperator at a power plant. One neural network is trained as a nonlinear simulation model of the process, then another network is trained to act as a combined state and parameter estimator for the process. The observer network incorporates smoothing of the parameter estimates in a form of regularization. A pole placement controller is designed which takes advantage of the sample-by-sample linearization and state estimates provided by the observer network. Simulation studies show that the nonlinear observer-based control loop performs better than a similar control loop based on a linear observer