Optimal control of batch reactor via structure approaching hybrid neural networks
A complex exothermic batch reactor model was developed by using structure approaching hybrid neural networks(SAHNN).The optimal reactor temperature profiles were obtained via the PSO-SQP algorithm solving maximum product concentration problem based on recurrent neural network(RNN).Considering model-plant mismatches and unmeasured disturbances,a novel extended integral square error index(EISE)was proposed,which introduced mismatches of model-plant into the optimal control profile.The approach used a feedback channel for the control and therefore dramatically enhanced the robustness and anti-disturbance performance.The stability analysis of the one-step-ahead control strategy through SAHNN-based model was described based on Lyapunov theory in detail.The result fully demonstrated the effectiveness of the proposed optimal control profile.