System identification and height control of laser cladding using adaptive neuro-fuzzy inference systems

Abstract

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.