GA-ANFIS PID compensated model reference adaptive control for BLDC motor

Adaptive control is one of the widely used control strategies to design advanced control systems for better performance and accuracy. Model reference adaptive control (MRAC) is a direct adaptive strategy with some adjustable controller parameters and an adjusting mechanism to adjust them. In this work Model Reference Adaptive Control for BLDC motors has been designed with a PID controller tuned by GA-ANFIS. GA-Trained ANFIS framework for tuning the PID controller has been proposed. This is used along with the MRAC to deliver enhanced performance in the control of BLDC motor. The performance of the proposed approach is validated for motor control under conditions of change in speed, change in load, change in inertia and change in phase resistance.. The performance is validated against convention PID and self tuning PID controllers. The result demonstrates a superior performance of the proposed approach Normal 0 false false false EN-IN X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;}

[1]  Keigo Watanabe,et al.  An optimized Takagi-Sugeno type neuro-fuzzy system for modeling robot manipulators , 2006, Neural Computing & Applications.

[2]  Mehmet Karaköse,et al.  A multi-objective artificial immune algorithm for parameter optimization in support vector machine , 2011, Appl. Soft Comput..

[3]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[4]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[5]  Murali Dasari,et al.  Modeling of a commercial BLDC motor and control using GA-ANFIS tuned PID controller , 2017, 2017 International Conference on Innovative Research In Electrical Sciences (IICIRES).

[6]  Mojtaba Ahmadieh Khanesar,et al.  Identification using ANFIS with intelligent hybrid stable learning algorithm approaches and stability analysis of training methods , 2009, Appl. Soft Comput..

[7]  Luca Maria Gambardella,et al.  Ant colony system: a cooperative learning approach to the traveling salesman problem , 1997, IEEE Trans. Evol. Comput..

[8]  A.M. Abdel Ghany,et al.  A novel fuzzy self tuning technique of single neuron PID controller for brushless DC motor , 2017, 2017 Nineteenth International Middle East Power Systems Conference (MEPCON).

[9]  A. Sedigh,et al.  Training ANFIS system with DE algorithm , 2011, The Fourth International Workshop on Advanced Computational Intelligence.

[10]  Lei Jin-li.  Adaptive Control for Brushless DC Motor Based on Fuzzy Inference , 2014 .

[11]  Mohammad Teshnehlab,et al.  Identification using ANFIS with intelligent hybrid stable learning algorithm approaches , 2009, Neural Computing and Applications.

[12]  Mohammad Teshnehlab,et al.  Novel Hybrid Learning Algorithms for Tuning ANFIS Parameters Using Adaptive Weighted PSO , 2007, 2007 IEEE International Fuzzy Systems Conference.

[13]  Mohamed. A. Shamseldin,et al.  Performance Study of Enhanced Non-Linear PID Control Applied on Brushless DC Motor , 2018, International Journal of Power Electronics and Drive Systems (IJPEDS).

[14]  S. P. Dubey,et al.  Comparative Study of Fuzzy Logic Based Speed Control of Multilevel Inverter fed Brushless DC Motor Drive , 2014 .