摘要
针对工业过程中存在时滞、非线性以及受控对象参数时变等特点,提出了一种基于龙格-库塔和灰色模型的神经元模糊自整定PID控制算法。该算法将被控对象视为灰色系统,并与四阶龙格-库塔结合建立系统的预测模型,以此克服时滞对系统的影响。利用神经元学习功能和模糊控制调节神经元增益克服系统的非线性和时变等问题。仿真结果表明,该控制算法鲁棒性强,响应速度快,具有工业应用价值。
To the existed characteristics of time-delay, non-linear and time-varying of parameters in the industry process, put forward an adaptive neuron-fuzzy PID control algorithm based on Runge-Kutta and grey model. The algorithm make the object as a grey system, and four order Runge-Kutta in combination with established system to forecast a model, to overcome the influence of time delay on the system. Use of neuronal learning function and fuzzy control regulation of neuronal gain system to overcome the nonlinear and time-varying problem. The simulation results show that the control algorithm is robust, fast response speed, good industrial application value.
出处
《工业仪表与自动化装置》
2013年第1期6-8,共3页
Industrial Instrumentation & Automation
基金
甘肃省教育厅科研资助项目(00330715-01)
兰州石化职业技术学院科研项目(k06-08)
