Analysis and Implementation of ANFIS-based Rotor Position Controller for BLDC Motors

This study proposes an adaptive neuro-fuzzy inference system (ANFIS)-based rotor position controller for brushless direct current (BLDC) motors to improve the control performance of the drive under transient and steady-state conditions. The dynamic response of a BLDC motor to the proposed ANFIS controller is considered as standard reference input. The effectiveness of the proposed controller is compared with that of the proportional integral derivative (PID) controller and fuzzy PID controller. The proposed controller solves the problem of nonlinearities and uncertainties caused by the reference input changes of BLDC motors and guarantees a fast and accurate dynamic response with an outstanding steady-state performance. Furthermore, the ANFIS controller provides low torque ripples and high starting torque. The detailed study includes a MATLAB-based simulation and an experimental prototype to illustrate the feasibility of the proposed topology.

[1]  A. Rubaai,et al.  Design and Implementation of Parallel Fuzzy PID Controller for High-Performance Brushless Motor Drives: An Integrated Environment for Rapid Control Prototyping , 2008, IEEE Transactions on Industry Applications.

[2]  Sathyan Murugan,et al.  TCAS Functioning and Enhancements , 2010 .

[3]  Muammer Gökbulut,et al.  A Hybrid Neuro-Fuzzy Controller for Brushless DC Motors , 2005, TAINN.

[4]  Ali Emadi,et al.  An FPGA-Based Novel Digital PWM Control Scheme for BLDC Motor Drives , 2009, IEEE Transactions on Industrial Electronics.

[5]  John Y. Hung,et al.  Variable structure control: a survey , 1993, IEEE Trans. Ind. Electron..

[6]  Metin Demirtas,et al.  Off-Line Tuning of a PI Speed Controller for a Permanent Magnet Brushless DC Motor using DSP , 2011 .

[7]  C. Senthil Kumar,et al.  Design and Implementation of Adaptive Fuzzy Controller for Speed Control of Brushless DC Motors , 2010 .

[8]  Angelo Raciti,et al.  A robust adaptive controller for PM motor drives in robotic applications , 1995 .

[9]  N. Hemati,et al.  Robust nonlinear control of brushless DC motors for direct-drive robotic applications , 1990 .

[10]  H. Moghbelli,et al.  Speed control of a brushless DC motor drive via adaptive neuro-fuzzy controller based on emotional learning algorithm , 2005, 2005 International Conference on Electrical Machines and Systems.

[11]  K. Baskaran,et al.  Implementation of a Fuzzy PI Controller for Speed Control of Induction Motors Using FPGA , 2010 .

[12]  Jian Sun,et al.  BLDC motor speed control system fault diagnosis based on LRGF neural network and adaptive lifting scheme , 2014, Appl. Soft Comput..

[13]  Ching-Hung Lee,et al.  Identification and control of dynamic systems using recurrent fuzzy neural networks , 2000, IEEE Trans. Fuzzy Syst..

[14]  Omer Aydogdu,et al.  Realization of Fuzzy Logic Controlled Brushless DC Motor Drives Using Matlab/Simulink , 2010 .

[15]  Badrilal Mathur,et al.  Adaptive Controllers for Permanent Magnet Brushless DC Motor Drive System using Adaptive-Network-based Fuzzy Interference System , 2011 .

[16]  Meng Joo Er,et al.  Robust adaptive control of robot manipulators using generalized fuzzy neural networks , 2003, IEEE Trans. Ind. Electron..

[17]  Mithun M. Bhaskar,et al.  A New Fuzzy Logic based Modeling and Simulation of a Switched Reluctance Motor , 2010 .

[18]  João Carlos Basilio,et al.  Design of PI and PID controllers with transient performance specification , 2002, IEEE Trans. Educ..

[19]  V. R. Jisha,et al.  Speed control of Brushless DC Motor: A comparative study , 2012, 2012 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES).

[20]  B. V. Manikandan,et al.  Adaptive Neuro-Fuzzy Inference System based speed controller for brushless DC motor , 2014, Neurocomputing.