Batch-to-batch Control of Batch Processes Based on Multilayer Recurrent Fuzzy Neural Network

摘要

The batch-to-batch model-based iterative optimal control strategy for batch processes is realized based on multilayer recurrent fuzzy neural network (MRFNN) and chaotic search. MRFNNs are used to model batch processes. Modeling and optimization problems are mainly solved by chaotic search. Due to model-plant mismatches and disturbances, the calculated optimal control profile may not be optimal when applied to the actual process. Current predictions are improved by prediction errors from previous batches, and the model errors are gradually reduced from batch-to-batch. Furthermore, the control strategy is developed for temperature tracking control. The effectiveness is verified on simulated batch reactors.