【摘要】 针对间歇过程,基于多层递归模糊神经网络和混沌搜索实现了终点产品质量的批次间迭代控制策略,并在此基础上提出了间歇过程温度控制的批次间迭代控制策略。多层递归模糊神经网络被用于间歇过程对象建模,混沌搜索用于过程建模和优化计算。由于存在模型误差和未知干扰,基于模型所计算出来的最优控制输入在实际运用到对象上后并不是最优的。利用间歇过程的重复特性,根据以前批次的模型预测误差来修正模型预测,并据此计算下一批次的最优控制输入。随着批次的进行,跟踪误差逐渐减小。仿真实验验证了该方法的有效性。更多还原

【Abstract】 Based on multilayer recurrent fuzzy neural network(MRFNN) and chaotic search(CS),the batch-to-batch iterative control strategy for final quality control in batch processes is realized.Furthermore,the strategy for temperature control in batch processes is proposed.Batch processes are modeled by MRFNN,and CS is employed for model training and optimization computation.Due to model-plant mismatches and unknown disturbances,the calculated optimal control profile may not be optimal when applied to the actual batch process.By utilizing the repetitive nature of batch process,model predictions are modified by prediction errors from previous batches,and the tracking errors are gradually reduced from batch-to-batch.The effectiveness is verified by simulation of batch reactors.更多还原