Simulation and Function Approximation Based Iterative Approach To Process Control

基于仿真和函数逼近的过程控制迭代方法

• 批准号: 0301993
• 负责人: Jay Lee
• 金额: $ 21.42万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-04-15 至 2007-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0301993&HistoricalAwards=false
• 关键词: Simulation; Function; Approximation; Based; Iterative

Research:The PI plans to investigate a simulation and function approximation-based strategy for bringing an evolutionary improvement to a control policy. The investigation is motivated by some inherent limitations of the current model-based predictive control formulation with respect to handling systems of large-scale complex dynamics, and large amount of uncertainty. The development will be rooted in an approach developed in the field of artificial intelligence - referred to by various names such as Neuro-Dynamic Programming and Reinforced Learning - which has shown great success in handling highly complex multi-stage discrete decision problems like backgammon playing, elevator dispatch problem, and job-shop scheduling. The approach, when extrapolated to the problem of process control, begins by performing closed-loop simulations with a given suboptimal control policy for an extensive set of possible operating conditions. The simulation results are then used to generate data for state versus "cost-to-go" or "reward" function, typically by fitting a neural network to the data. The approximation is improved by additional off-line calculations, either by "value iteration" based on iterating the Bellman Equation or by "policy iteration" based on iterating between policy evaluation and policy improvement. The improved approximation of the "cost-to-go" function can be used to implement optimal control in a computationally efficient way, either by reducing a large-horizon problem into an equivalent short-horizon problem or by allowing an off-line parameterization of the improved control law. To make the approach practicable for process control a number of issues need to be resolved. The success of the approach will depend on the ability to obtain an accurate and robust approximation of the cost-to-go function. An immediate question to ask is what types of function approximators are best suited for the approximation. Also, the level of confidence in the neural network's cost predictions through interpolation and extrapolation need to be taken into account in the control calculations. The PI plans to investigate these and other fundamental issues to arrive at systematic and practically useful answers. The PI will collaborate with industrial partners Weyerhauser, Owens Corning, LG Chemicals, and Aspen Technology, to test the developed tools on real industrial process and to incorporate them into commercial process control software packages. The application portion of the project will be carried out by students and visitors supported by these companies.Broader Impact:The Chemical Process Industries (CPI) are replete with nonlinear control problems involving significant uncertainties, which can benefit from this work. In addition to process control, the strategy fits naturally to planning and scheduling problems under uncertainty as well as supply chain operation problems.

研究：PI计划研究一种基于模拟和函数近似的策略，用于对控制策略进行进化改进。 调查的动机是一些固有的局限性，目前基于模型的预测控制制定处理系统的大规模复杂的动态，大量的不确定性。 开发将植根于人工智能领域开发的方法-被称为各种名称，如神经动态规划和强化学习-在处理高度复杂的多阶段离散决策问题方面取得了巨大成功，如西洋双陆棋，电梯调度问题和车间调度。 该方法，当外推到过程控制的问题，开始进行闭环模拟与一个给定的次优控制策略的一组广泛的可能的操作条件。 然后，模拟结果用于生成状态与“成本”或“奖励”函数的数据，通常通过将神经网络拟合到数据。 通过额外的离线计算，或者通过基于贝尔曼方程迭代的“值迭代”，或者通过基于策略评估和策略改进之间的迭代的“策略迭代”，来改进近似。 改进的近似的“成本去”功能可以用来实现最优控制在计算上有效的方式，通过减少一个大的地平线问题到一个等效的短期问题，或通过允许离线参数化的改进的控制律。为了使该方法在过程控制中切实可行，需要解决一些问题。 该方法的成功将取决于能否获得一个准确和稳健的剩余成本函数近似值。 一个直接的问题是什么类型的函数逼近器最适合近似。 此外，在控制计算中需要考虑通过内插和外推的神经网络成本预测的置信水平。 PI计划调查这些和其他基本问题，以获得系统和实际有用的答案。 PI将与工业合作伙伴Weyerhauser，Owens Corning，LG Chemicals和白杨Technology合作，在真实的工业过程中测试开发的工具，并将其纳入商业过程控制软件包。 该项目的应用部分将由这些公司支持的学生和参观者进行。更广泛的影响：化学过程工业（CPI）充满了涉及重大不确定性的非线性控制问题，这可以从这项工作中受益。 除了过程控制之外，该策略还适用于不确定性下的计划和调度问题以及供应链运作问题。