Abstract
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 attemporator 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 the form of regularization. A pole placement controller is designed which takes advantage of the sample-by-sample linearizations 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.
Original language | English |
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Title of host publication | 2000 American Control Conference, Chicago, Illinois, USA, June 28-30, 2000 |
Publication date | 2000 |
Publication status | Published - 2000 |
Event | Simulation, State Estimation and Control of Nonlinear Superheater Attemporator using Neural Networks - Duration: 19 May 2010 → … |
Conference
Conference | Simulation, State Estimation and Control of Nonlinear Superheater Attemporator using Neural Networks |
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Period | 19/05/2010 → … |