Presenter: Hesam Hassanpour
Supervisor(s): Dr. Mhaskar
Project Description: This work addresses one aspect of the overparameterization problem in using recurrent neural networks (RNN) based models for model predictive control (MPC) implementations. An approach is proposed to handle situations where the training data may not be sufficiently rich, and in particular, for handling historical data with correlated inputs. Two methods are proposed. The key idea in the first method is to perform principal component analysis (PCA) on input data and use scores to build a PCA-RNN model. A PCA-RNN-based MPC is then designed to calculate the optimal scores and subsequently determine the manipulated inputs. An alternative solution is proposed in the second approach by applying a new constraint on squared prediction error (SPE) statistic in the RNN-based MPC to make prescribed inputs follow the PCA model constructed for training input data. Finally, an approach is presented that allows for breaking the correlation in the MPC implementation while maintaining model validity. This is done by first generating richer closed-loop data by implementing the SPE-based MPC with slightly relaxed constraints. The new data is then used to re-identify the model, and for use in the MPC. The efficacy of the proposed approaches to handle the problem of set-point tracking is evaluated using a chemical reactor example. The results are compared with a nominal MPC design, and the superior performance under the proposed formulations is demonstrated.
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