An Effective Mechanism to Control Servo Motor Using Adaptive Neuro-Fuzzy Inference System
This paper proposes a method to observe the main properties of servo motor such as speed and torque. Servo motor is a high performance and critical device because it can be operated at same performance even at high RPM (i.e.) above 1000 RPM. In order to maintain its performance and also to prevent any mishap due to its high speed, we need a feedback system to control the servo motor speed and torque. In general servo motor has inbuilt feedback system which consist of positional sensors in order to control the position and speed. In this paper we are going to control the servo motor using artificial neural fuzzy circuits with fuzzy observer estimator. Adaptive neuro fuzzy inference system uses the inbuilt positional feedback sensor to control the servo motor. Hence any error captured by the observer estimator will fed as a feedback to the input ANFIS and then controller will make decision to make the servo motor attain its precision over speed and torque. ANFIS is a an artificial neural network based system hence we will using some consistent input data sets to test and train the network. Based on the output predicted by comparing the training and testing data sets, the controller will control the servo motor. Controller is the heart of the servo motor system which in turn takes responsible to control, maintain, and prevent any nonlinear or inconsistent system.