非线性大工业过程稳态模型的强一致性分析

Strong Consistency Analysis of Steady-State Models for Nonlinear Large-Scale Industrial Processes

稳态优化问题就是依据过程的数学模型,在约束条件下,优化其目标函数,而实际的工业过程往往是呈非线性或慢时变性。针对动态非线性大工业过程, 提出了得到其可分稳态模型强一致性估计的分散辨识方法;利用多项式对非线性函数的无限逼近的性质和优化过程中设定点的阶跃信号作输入激励信号,获得了动态非线性大工业过程的可分稳态模型和可辨识的条件。

The steady-state optimization problem is optimizing objective functions based on mathematical model under constrained conditions, in fact the large-scale industrial processes are often nonlinear and slowly time varying. In allusion to dynamic nonlinear large-scale industrial processes, to bring up gained the method of decentralized identification for the strong consistency estimates of the divisible steady-state models, it is used that property of polynomial can infinitely approach to the nonlinear function and in optimization processes use step signals as input signals, the divisible steady-state models of dynamic nonlinear large-scale industrial processes, and the cognizable conditions are obtained.