Improved Self-optimizing Control Method Based on Piece-wise Linearization
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摘要: 现有自主优化控制方法(Self-optimizing control, SOC)均基于系统名义工作点的线性化模型,过程的非线性较强时, 由线性化误差导致的损失将显著影响控制系统的自主优化性能.提出了一种基于分段线性化模型的改进自主优化控制方法, 获取不同扰动工况下的分段线性化模型,分别应用线性化SOC方法并构造总的被控变量. 新方法能够在更大范围的操作空间内最小化平均损失,具有更加优异的自主优化效果. 对一个数值算例和一个放热反应过程的研究验证了提出方法的有效性.Abstract: The existing self-optimizing control (SOC) methods are all based on the linearized model around the nominal point, which may cause big objective function loss due to linearization error when the process is highly nonlinear. This paper presents an improved SOC method based on piece-wise linearization. Piece-wise linearized models are obtained at different operation points, then the linear SOC method is applied and the controlled variables are synthesized finally. The new method minimizes the average loss in a wider range hence the self-optimizing performance is better. A numerical case and an exothermic reactor are studied to demonstrate the usage of the proposed method.
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