(17d) State Estimation and Model Predictive Control of Nonlinear Processes Using Recurrent Neural Network Models
AIChE Annual Meeting
2020
2020 Virtual AIChE Annual Meeting
Computing and Systems Technology Division
Estimation and Control Under Uncertainty
Monday, November 16, 2020 - 8:45am to 9:00am
To address this issue, this work develops RNN-based state observer and MPC for nonlinear systems. A well-fitting RNN model is used to predict nonlinear processes dynamics. Dataset will be generated from extensive open-loop simulations within a predefined operation region to train and develop RNN model that will capture process dynamics for a general class of nonlinear systems. By using the measured states, the state observer will use the RNN model to estimate the unmeasured states. Afterward, the Lyapunov-based MPC (LMPC) that also utilizes the developed RNN models as the prediction model is developed to achieve closed-loop stability in terms of having the closed-loop system states to be bounded within the stability region for all times and eventually will converge to a small neighborhood around the origin. A chemical reactor example will be used to demonstrate the performance of the LMPC using RNN-based observer.
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