摘要
针对目前球磨机系统控制存在的不足,提出了一种基于运行模式识别的球磨机系统自适应模糊解耦控制算法。首先,分析了球磨机系统的动态特性,并结合某电厂测试试验,得到了球磨机系统的数学模型;其次,采用LS-SVM算法对球磨机系统的运行状态进行识别和分类;对于正常工况,针对耦合回路,设计了自适应解耦模糊控制器对球磨机进行控制;对于异常工况,给出了相应的控制策略。最后对正常工况下的自适应解耦模糊控制算法进行了仿真研究。仿真结果表明,该算法能够有效地实现球磨机系统运行工况模式识别和耦合回路的解耦控制,控制系统具有良好的动态性和鲁棒性。
In the light of the deficiencies in the control of current ball mill systems,proposed was a self-adaptive fuzzy decoupling control algorithm for ball mill systems based on operating mode identification.Firstly,the authors have analyzed the dynamic characteristics of a ball mill system and in combination with a test performed in a power plant,obtained a mathematical model for the ball mill systems.Secondly,the authors have used the least square supportive vector machine(LS-SVM) algorithm to identify and classify the operating state of the ball mill system.For normal operating conditions,a self-adaptive decoupling fuzzy controller was designed for the coupling loop to control the ball mill.For abnormal operating conditions,corresponding control tactics were given.Finally,a simulation study was performed of the algorithm in question at normal operating conditions.The simulation results show that the algorithm can effectively realize the identification of the ball mill system operating mode and the decoupling control of the coupling loops.The control system exhibit relatively good dynamic properties and robustness.
出处
《热能动力工程》
CAS
CSCD
北大核心
2009年第4期481-485,共5页
Journal of Engineering for Thermal Energy and Power
基金
国家自然科学基金资助项目(50775035)
江苏省高技术研究基金资助项目(BG2005033)
关键词
球磨机系统
模式识别
最小二乘支持向量机
自适应模糊控制
解耦控制
ball mill system,mode identification,least square supportive vector machine,adaptive fuzzy control,decoupling control
