System identification and height control of laser cladding using adaptive neuro-fuzzy inference systems
System identification and height control of laser cladding using adaptive neuro-fuzzy inference systems
复制标题
使用自适应神经模糊推理系统的激光熔覆系统识别和高度控制
DOI：
10.2351/1.5062940
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Adrian Gelrich
中科院分区：
文献类型：
--
作者：
M. H. Farshidianfar;A. Khajepour;M. Zeinali;Adrian Gelrich
采用自适应神经模糊推理系统（ANFIS）对激光熔覆过程中的熔覆高度进行识别和控制。在控制器中以基片的扫描速度作为控制动作。利用CCD相机获取反馈信号。首先，通过混合学习算法，利用ANFIS网络对过程进行识别。在ANFIS逆学习方案中得到了ANFIS对象的逆动力学。将逆动力学应用于神经模糊结构中，得到该过程的ANFIS控制器。通过调整ANFIS控制器作为一个组合单元，设计了一个完整的控制系统。在过程建模和过程控制方面均取得了满意的结果。采用自适应神经模糊推理系统（ANFIS）对激光熔覆过程中的熔覆高度进行识别和控制。在控制器中以基片的扫描速度作为控制动作。利用CCD相机获取反馈信号。首先，通过混合学习算法，利用ANFIS网络对过程进行识别。在ANFIS逆学习方案中得到了ANFIS对象的逆动力学。将逆动力学应用于神经模糊结构中，得到该过程的ANFIS控制器。通过调整ANFIS控制器作为一个组合单元，设计了一个完整的控制系统。在过程建模和过程控制方面均取得了满意的结果。
Adaptive neuro-fuzzy inference systems (ANFIS) are utilized to identify and control the clad height in the laser cladding process. The scanning speed of the substrate is used as the control action in the controller. A feedback signal is obtained using a CCD camera. First, the process is identified by means of an ANFIS network through a hybrid learning algorithm. The inverse dynamics of the ANFIS plant is later obtained in an ANFIS inverse learning scheme. The inverse dynamics is used in a neuro-fuzzy structure to obtain an ANFIS controller for the process. A complete control system is designed by tuning the ANFIS controller as a combined unit. Satisfactory results are obtained both in process modeling and process control.Adaptive neuro-fuzzy inference systems (ANFIS) are utilized to identify and control the clad height in the laser cladding process. The scanning speed of the substrate is used as the control action in the controller. A feedback signal is obtained using a CCD camera. First, the process is identified by means of an ANFIS network through a hybrid learning algorithm. The inverse dynamics of the ANFIS plant is later obtained in an ANFIS inverse learning scheme. The inverse dynamics is used in a neuro-fuzzy structure to obtain an ANFIS controller for the process. A complete control system is designed by tuning the ANFIS controller as a combined unit. Satisfactory results are obtained both in process modeling and process control.