title: Grouped-neural network modeling for model predictive control.
abstract: A group of feed-forward neural networks (NNs), each providing the prediction of an individual process output at a future step, is used as the dynamic prediction model for the model-based predictive control (MPC) scheme in the proposed work. These NNs are parallel (independent) rather than cascaded--they are trained and implemented in parallel. Therefore, the complexity and effort in the training stage is decreased and compounded error propagation is eliminated from the prediction. A new strategy of compensating for the process-model mismatch under this grouped-NN model structure is also developed. Effectiveness of the scheme as a general nonlinear MPC is demonstrated by simulation results.
authors.affiliation: School of Chemical Engineering, Oklahoma State University, Stillwater 74078-5021, USA.
authors.forename: Jing
authors.initials: J
authors.lastname: Ou
authors.forename: R Russell
authors.initials: RR
authors.lastname: Rhinehart
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D005246:Feedback; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D011786:Quality Control; D012212:Rheology / Q000379:methods; D012680:Sensitivity and Specificity; D013269:Stochastic Processes