Artificial Neuro-Fuzzy Inference System (ANFIS) Based Speed Control of Separately Excited DC Motor for Load Torque Variations

Abstract

Separately excited direct Motor (SEDCM) is an electromechanical actuator used to power different loads across several industrial and domestic applications. One fundamental characteristic for controlling when driving is the motor's speed. The external load linked to the drive negatively impacts speed if the controller is weak and the load varies. Objective of this thesis work is to design an Artificial Neuro-Fuzzy Inference System (ANFIS)-based speed control mechanism for a separately excited DC motor under varying load torque conditions. The ANFIS controller integrates neural networks and fuzzy logic to improve motor speed regulation, ensuring robust performance despite disturbances in load torque. Additionally, this this work explores the effectiveness of armature voltage control (source voltage adjustment) for dynamically regulating motor speed, comparing its performance with conventional control strategies. ANFIS, fuzzy logic controllers, PID controllers, and open loop (without controller) have all been used to measure the speed of an independently stimulated SEDC motor. At first, the motor speed can be regulated and modified by changing the armature voltage (the input supply voltage).When the torque of the load grows and the transient and steady state faults rise, the motor's speed falls in the absence of a controller. The motor performs poorly as a result, and its speed will not maintain its rated level. A PID controller improves the motor's speed over an open loop, but the overall performance is still poor and there are still some transient and steady state issues. Although fuzzy logic controllers perform better than PID controllers in terms of system performance, the speed still fluctuates as the torque of the load changes. However, ANFIS better than fuzzy, PID, and open loop control systems, operates at its rated speed, has low steady state and transient errors, and keeps the motor's speed constant as the load increases. In conclusion, NFIS is superior to fuzzy and PID controllers due to its zero overshoot and reduced 38.31% settling time compared to fuzzy and 50.93 % compared to PID, reduced 37.12% rise time compared to fuzzy and 44.87 % compared to PID, and reduced 85% steady-state error compared to fuzzy and 98.5 % compared to PID. Additionally, by resolving the motor's nonlinear properties, the system's overall performance will improve.