GOALI: Nonlinear Identification and Control Strategies

利用非线性动态模型的基于Seborg goali模型的过程控制策略可以比传统的基于线性动态模型的PID和控制策略提供更好的控制效果。成功应用非线性控制系统的一个因素是能够获得一个相当准确的动态模型。对于一些工业控制应用，物理模型可以从非稳态质量和能量平衡等基本原理发展而来。然而，由于模型复杂、模型参数未知或缺少关键测量，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，包括过程复杂、缺乏对过程的理解、有限的测量以及开发这些模型所需的时间和精力。对于这些情况，一个有吸引力的选择是利用非线性辨识技术建立一个与已有先验物理信息一致的经验非线性模型。在这个联合的学术和工业研究项目中，加州大学圣巴巴拉分校和杜邦大学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和广泛应用。这些问题包括：(1)输入序列的选择以确保适当的过程激励；(2)加入现有的先验过程知识；(3)关键设计参数的选择，如采样周期、要获取的数据量、模型阶数和模型时延；(4)模型不确定性的表征；以及(5)在线模型更新和自适应。这项研究产生的识别方法将在一些模拟和实验研究中进行评估，包括工业应用。一个案例研究将提供备选识别技术的比较，部分基于这些技术是否可用于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.