基于结构逼近式神经网络的间歇反应器优化控制

利用结构逼近式混合神经网络（SAHNN）建立了一类典型放热液相二级平行间歇反应的数学模型。基于主产物浓度和反应温度的递归神经网络（RNN）模型，使用混合PSO-SQP算法求解该间歇反应主产物产率最大化问题，进而得到反应温度优化曲线。鉴于反应温度实时可测，提出扩展的EISE指标，该指标把实时计算的模型误差引入控制策略，为基于模型的控制增加了反馈通道，增强了控制方法的鲁棒性和抗干扰性能。利用 原理对所提出的一步超前预测控制做了稳定性分析，证明了算法的正确性。研究的结果充分证明了基于SAHNN混合神经网络模型的优化控制策略的有效性。

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.