基于改进微分进化和分散辨识算法的炼钢模型研究

在炼钢过程模型辨识中，被控对象的动态特性往往表现出非线性、慢时变、大迟延和不确定性等特点，使得难以对其建立比较精确的模型。为实现精确建模，提出了一种基于微分进化和分散辨识算法的辨识方法。该方法通过改进的微分进化算法，对系统进行参数优化，接着采用分散辨识在设定点输入阶跃信号，待系统进入稳态后再采样，使得到的稳态输出值能够更快、更精确地逼近实际系统的输出，达到精确建模的目的。仿真结果表明，通过微分进化算法可进一步确定炼钢过程的最佳参数，在采用分散辨识方法对炼钢复杂对象进行辨识后，可以建立更好的数学模型。

Improving Steel- making Model Based on Differential Evolution and Dispersion Identification Algorithm

In the model identification of steelmaking process, the dynamic characteristics of the controlled object often performs in a nonlinear, slow time - varying delay and uncertain way, which makes it difficult to establish a more precise model. For the purpose of accurate modeling, this paper proposes an identification method based on differential evolution and decentralized identification algorithm. In this method, improved differential evolution algorithm was used to optimize system parameters, and then decentralized identification was used to input step signal at set point. Next, sampling was performed when the system enters a steady state, so as to record an output value closer to the output of actual system at fast pace and finally achieve accurate modeling. Simulation results show that differential evolution algorithm can be used to determine the optimum parameter during steel production, and a better mathematical model can be created after using decentralized identification to identify the complex object of steel production.