Nonlinear Model Predictive Control
Nonlinear Model Predictive Control
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DOI：
10.1007/978-0-85729-398-5_9
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发表时间：
2007
期刊：
影响因子：
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通讯作者：
Eduardo F. Camacho;C. Bordons
中科院分区：
文献类型：
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作者：
Eduardo F. Camacho;C. Bordons
一般来说，工业过程是非线性的，但是，正如本书所示，大多数mpp应用都是基于线性模型的使用。这主要有两个原因：一方面，基于过程数据的线性模型的识别相对容易，另一方面，当工厂在工作点附近运行时，线性模型提供了良好的结果。在过程工业中，线性mpci广泛存在，其目标是使过程保持在平稳状态，而不是从一个操作点到另一个操作点进行频繁的变化，因此，精确的线性模型就足够了。此外，将线性模型与二次目标函数结合使用会产生一个凸问题（二次规划），该问题的解已经得到了很好的研究，并且有许多商业产品可用。在存在大量变量的过程中，能够保证在短于采样时间内得到收敛解的算法的存在是至关重要的。
In general, industrial processes are nonlinear, but, as has been shown in this book, mostMPCapplications are based on the use of linear models. There are two main reasons for this: on one hand, the identification of a linear model based on process data is relatively easy and, on the other hand, linear models provide good results when the plant is operating in the neighbourhood of the operating point. In the process industries, where linearMPCis widespread, the objective is to keep the process around the stationary state rather than perform frequent changes from one operation point to another and, therefore, a precise linear model is enough. Besides, the use of a linear model together with a quadratic objective function gives rise to a convex problem (Quadratic Programming) whose solution is well studied with many commercial products available. The existence of algorithms that can guarantee a convergent solution in a time shorter than the sampling time is crucial in processes where a great number of variables appear.