论文标题
在非线性模型中包括稳态信息：软传感器的开发应用
Including steady-state information in nonlinear models: an application to the development of soft-sensors
论文作者
论文摘要
当系统的动态数据仅在有限的操作范围内传达动态信息时，在更广泛的操作范围内具有良好性能的模型的识别非常不可能。然而，具有这种特征的模型可以实现现代控制系统。为了克服这样的缺点，本文描述了一种可以从动态数据和稳态信息中训练模型的方法，该方法被假定可用。新颖性是，该过程可以应用于具有相当复杂结构的模型，例如以双向目标的方式进行多层感知神经网络，而无需在分析上或数值上计算固定点。结果，所需的计算时间大大减少了。在数值示例中探讨了所提出的方法的功能，并开发软传感器，以估算真正的深水近海油井。结果表明该过程产生具有良好动力学和静态性能的合适的软传感器，并且在参数中非线性的模型的情况下，考虑到现有方法，计算时间的增益约为三个数量级。
When the dynamical data of a system only convey dynamic information over a limited operating range, the identification of models with good performance over a wider operating range is very unlikely. Nevertheless, models with such characteristic are desirable to implement modern control systems. To overcome such a shortcoming, this paper describes a methodology to train models from dynamical data and steady-state information, which is assumed available. The novelty is that the procedure can be applied to models with rather complex structures such as multilayer perceptron neural networks in a bi-objective fashion without the need to compute fixed points neither analytically nor numerically. As a consequence, the required computing time is greatly reduced. The capabilities of the proposed method are explored in numerical examples and the development of soft-sensors for downhole pressure estimation for a real deep-water offshore oil well. The results indicate that the procedure yields suitable soft-sensors with good dynamical and static performance and, in the case of models that are nonlinear in the parameters, the gain in computation time is about three orders of magnitude considering existing approaches.
