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On Handling Cost Gradient Uncertainty in Real-Time Optimization

机译:实时优化中处理成本梯度不确定性的研究

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This paper deals with the real-time optimization of uncertain plants and proposes an approach based on surrogate models to reach the plant optimum when the plant cost gradient is imperfectly known. It is shown that, for processes with only box constraints, the optimum is reached upon convergence if the multiplicative gradient uncertainty lies within some bounded interval. For the case of general constraints, conditions are derived that guarantee plant feasibility and, in principle, allow enforcing cost decrease at each iteration.
机译:本文讨论了不确定工厂的实时优化问题,并提出了一种基于代理模型的方法,当不完全知道工厂成本梯度时可以达到工厂最优。结果表明,对于只有盒约束的过程,如果乘法梯度不确定性在某个有界区间内,则收敛时可以达到最佳。对于一般约束条件,需要推导条件以保证工厂的可行性,并在原则上允许每次迭代降低成本。

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