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首页> 外文会议>2011 Second International Conference on Digital Manufacturing Automation >Constrained Batch-to-Batch Optimal Control for Batch Process Based on Support Vector Regression Model with Batchwise Error Feedback
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Constrained Batch-to-Batch Optimal Control for Batch Process Based on Support Vector Regression Model with Batchwise Error Feedback

机译：基于支持向量回归模型的批量误差约束批间最优控制

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摘要

A batch-to-batch optimal control method is presented for batch processes under input and output constraints with batch wise error feedback. Generally it is very difficult to acquire an accurate mechanistic model for a batch process. Because support vector machine is powerful for the problems characterized by small samples, nonLinearity, high dimension and local minima, support vector regression model is developed for end-point optimal control of batch process. Because there exist model error and disturbances, an iterative (batch-to-batch) method is used to exploit the repetitive nature of batch processes to determine the optimal operating poLicy. To ensure the safe, smooth operations of batch process, certain constraints are taken into considered. Furthermore, batch wise error feedback is incorporated into the computation of the optimal operating poLicy to guarantee the convergence of the batch-to-batch optimal control. Numerical simulation shows that the method can improve the process performance through batch to batch under constraints.
机译：针对输入和输出约束下具有批处理误差反馈的批处理过程，提出了一种批到批的最优控制方法。通常，为批处理过程获取准确的机械模型非常困难。由于支持向量机对于样本量少，非线性，高维和局部极小等问题具有强大的功能，因此开发了支持向量回归模型以实现批处理过程的端点最优控制。由于存在模型误差和干扰，因此使用迭代（批到批）方法来利用批处理的重复性来确定最佳操作策略。为了确保批处理过程的安全，顺畅操作，需要考虑某些约束条件。此外，逐段误差反馈被合并到最佳运行策略的计算中，以确保逐批最佳控制的收敛性。数值模拟表明，该方法可以在约束条件下逐批提高工艺性能。

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