摘要
针对间歇生产,提出了一种基于广义预测控制的批次迭代优化控制策略———BGPC,在间歇过程中引入批次间优化的思想,将迭代学习控制ILC和广义预测控制GPC相结合,在GPC实时结构参数辨识的基础上利用前面批次的模型预测误差修正当前批次的模型预测值。该算法能够有效地克服模型失配、扰动和系统参数变化等情况。文章最后以一个数值例子和间歇反应器为对象进行仿真试验,验证了该算法是有效的。
A batch-to-batch iterative optimal control strategy general predictive control （GPC）-based for batch process-BGPC is proposed,which introduces the idea of batch-to-batch optimization into batch process. It uses model prediction errors from previous runs to improve current GPC model predictions based on the combination of GPC and iterative learning control （ILC）. This algorithm can effectively overcome the model mismatch, unknown disturbance and parameter variation. The effectiveness and robustness of the proposed scheme are illustrated and verified on a numerical case and a simulated batch reactor system.
出处
《化工自动化及仪表》
EI
CAS
2006年第2期25-28,共4页
Control and Instruments in Chemical Industry
基金
国家自然科学基金资助项目(20206028
20576116)
德国洪堡基金(王海清)
关键词
广义预测控制
迭代学习控制
间歇生产
最优控制
批次间优化
generalized predictive control
iterative learning control
batch process
optimal control
batch-to-batch optimization
