灰色广义预测控制系统的稳定性和鲁棒性研究

Stability and Robustness Research on Grey Generalized Prediction Control System

在文[1]中,针对贫信息、不确定性系统存在的不确定性、随机干扰以及系统特征参数随工作环境的变化而变化的特点,本文作者将具有广阔发展前景的灰色系统理论和广义预测控制理论结合起来,提出了一种新型的计算机控制策略:灰色广义预测控制算法(GGPC)。在本文中,我们将应用IMC结构理论,对该算法的稳定性、鲁棒性进行一些初步探讨,并给出了一个生态学方面应用的例子。实例仿真结果表明:GGPC算法具有较强的抗干扰能力,有较好的鲁棒性以及自适应能力,通过调整参数,可以获得非常满意的动、静态性能。

To the poor information and uncertain system featured by significant uncertainties and random disturbances and with system eigenparameters varying greatly with the working conditions and surroundings, a computer control method was firstly designed in paper one, which is named as grey generalized prediction control method (GGPC), and is a research combination of grey system theory and generalized prediction control theory. In the paper, the closed loop stability and robustness of the GGPC system is analyzed using the IMC structure, and some concise conclusions are obtained. The simulation results about bionomics show that the GGPC method has very strong robustness and adaptability as well as good resistant disturbances and satisfied performances. And based on all above-mentioned advantages of the GGPC system, the GGPC arithmetic would be widely used.