GOALI: Nonlinear Identification and Control Strategies

摘要- Seborg GOALI 利用非线性动态模型的基于模型的过程控制策略可以提供对基于线性动态模型的常规PID和控制策略的改进。 成功应用非线性控制系统的一个因素是合理精确的动态模型的可用性。 对于某些工业控制应用，物理模型可以从第一原理开发，例如非稳态质量和能量平衡。 然而，由于模型复杂性、未知的模型参数或缺乏关键测量，使用物理模型作为在线控制计算的一部分可能是不可行的。 在许多应用中，由于各种原因，无法获得准确的物理模型，这些原因包括工艺复杂性，缺乏工艺理解，测量有限以及开发它们所需的时间和精力。 对于这些情况下，一个有吸引力的替代方案是开发一个经验的非线性模型，这是一致的先验物理信息，使用非线性识别技术。 在这一学术-工业联合研究项目中，圣巴巴拉的加州大学和杜邦公司的过程控制研究人员将解决非线性识别中的一些问题，这些问题限制了现有方法的有效性及其广泛应用。 这些问题包括：（1）选择输入序列以确保适当的过程激励;（2）结合可用的先验过程知识;（3）选择关键设计参数，例如采样周期、待获取的数据量、模型阶数和模型时间延迟;（4）表征模型不确定性;以及（5）在线模型更新和自适应。 从研究中产生的识别方法将在一些模拟和实验研究，包括工业应用进行评估。 一个案例研究将提供一个替代识别技术的基础上，部分比较，他们是否可以用来开发有效的基于模型的控制系统。

Abstract - Seborg GOALI Model-based process control strategies utilizing nonlinear dynamic models can provide improvements over conventional PID and control strategies based on linear dynamic models. A factor for successful application of nonlinear control systems is the availability of a reasonably accurate, dynamic model. For some industrial control applications, physical models can be developed from first principles such as unsteady-state mass and energy balances. However, it may not be feasible to use a physical model as part of the on-line control calculations due to model complexity, unknown model parameters, or the lack of key measurements. In many applications, accurate physical models are not available for a variety of reasons, which include process complexity, lack of process understanding, limited measurements, and the time and effort required to develop them. For these situations, an attractive alternative is to develop an empirical nonlinear model which is consistent with available a priori physical information using nonlinear identification techniques. In this joint academic-industrial research project, process control researchers at the University of California at Santa Barbara and at DuPont will address a number of issues in nonlinear identification which limit the effectiveness of existing methods and their widespread application. These issues include: (1) selection of the input sequence to ensure appropriate process excitation; (2) incorporation of available a priori process knowledge; (3) selection of key design parameters such as sampling period, amount of data to be acquired, model orders and model time delay; (4) characterization of model uncertainty; and (5) on-line model updating and adaptation. The identification methods resulting from the research will be evaluated in a number of simulation and experimental studies, including industrial applications. A case study will provide a comparison of alternative identification techniques based, in part, on whether they can be used to develop effective model-based control systems.