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传统的预测函数控制通常采用一阶的预测模型,该预测模型不能完全表征被控对象,因此使得传统的预测函数控制的鲁棒性受到一定限制。特征模型是一种比动力学模型简单,但能表征被控对象特征的模型。针对预测函数控制算法的缺陷,提出了利用二阶特征模型来构成预测模型。通过与采用一阶预测模型的预测函数控制进行比较,理论分析与仿真实验结果均表明,采用特征模型的预测函数控制具有比一阶预测模型更好的控制效果,该算法在SUPCON-JX300X集散控制系统上实现。
The traditional predictive function control usually adopts the first-order predictive model, which can not fully characterize the controlled object, so the controllability of the traditional predictive function is limited. A feature model is a model that is simpler than a dynamic model but characterizes the characteristics of a controlled object. Aiming at the defect of the predictive function control algorithm, this paper proposes to construct the prediction model by using the second-order eigenmodel. Compared with the predictive function control using the first-order predictive model, both theoretical analysis and simulation results show that the predictive function control using the characteristic model has better control effect than the first-order predictive model. The algorithm is based on SUPCON-JX300X distributed control System to achieve.