ROBUST INPUT POLICIES FOR BATCH REACTORS UNDER PARAMETRIC UNCERTAINTY
ROBUST INPUT POLICIES FOR BATCH REACTORS UNDER PARAMETRIC UNCERTAINTY
复制标题
参数不确定性下间歇反应器的稳健输入策略
DOI：
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发表时间：
1995
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通讯作者：
M. Agarwal
中科院分区：
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作者：
P. Terwiesch;M. Agarwal
摘要间歇反应器的输入分布通常是在已知参数模型的假设下获得的。当这一假设不成立时，参数与标称值的偏差会严重影响标称优化的性能。极小极大优化提供了一种考虑参数不确定性的过敏性，但与名义优化相比，其固有的最坏情况假设使其在名义参数值附近的性能下降。这项工作提出了一种新的优化过程，它提供了类似于极小极大优化的稳健性，同时保持了与标称优化类似的标称性能。该方法在给定前一辨识步骤中的不确定过程参数的概率分布的情况下，优化整个参数空间的成本函数期望，而不是优化参数期望的成本函数。通过这种方式，增强了对不确定或时变参数的鲁棒性。
Abstract Batch-reactor input profiles are normally obtained under the assumption of knowledge of a parametric model. When this assumption does not hold, parameter deviation from the nominal value can severely impair performance of the nominal optimization. The minimax optimization offers an allernative that accounts for parameteric uncertainty, but its inherent worst-case assumption degrades its performance near the nominal parameter value compared to that of the nominal optimization. This work presents a new optimization procedure that offers robustness similar to the minimax optimization while retaining nominal performance similar to the nominal optimization. Given a probability distribution for the uncertain process parameters from a previous identification step, the method optimizes the expectation of cost function for the entire parameter space instead of optimizing the cost function for the expectation of the parameters. In this way increased robustness towards uncertain or time-varying parameters i...