Brushless DC Motor Controller Optimization Using Simulated Annealing

The proportional-integral (PI) and proportional-integral-derivative (PID) controllers are widely used in Brushless Direct Current (BLDC) motors. To accommodate the high performance requirement of the modern industry, optimization of the PI/PID parameters are broadly investigated, and several tuning methods have been proposed. This work illustrates a robust current and speed controllers design method for permanent magnet brushless dc motor (PMBLDCM) where a simulated annealing algorithm is employed to tune the parameters of a PI current controller and a PID speed controller. During the tuning process of controller parameters, three response performance parameters (overshoot, rise time, and settling time) are simultaneously optimized. The technique is compared to both Particle Swarm Optimization and Ziegler-Nichols tuning methods and experimental results show the superiority of the proposed approach.

[1]  Leang-San Shieh,et al.  DSP-Based PID Controller Design for the PMDC Motor , 2006 .

[2]  Peter Salamon,et al.  Facts, Conjectures, and Improvements for Simulated Annealing , 1987 .

[3]  A. R. Millner.  Multi-hundred horsepower permanent magnet brushless disc motors , 1994, Proceedings of 1994 IEEE Applied Power Electronics Conference and Exposition - ASPEC'94.

[4]  Ching Chuen Chan,et al.  An overview of power electronics in electric vehicles , 1997, IEEE Trans. Ind. Electron..

[5]  Changliang Xia,et al.  Speed control of brushless DC motor using genetic algorithm based fuzzy controller , 2004, 2004 International Conference on Intelligent Mechatronics and Automation, 2004. Proceedings..

[6]  Padmaraja Yedamale,et al.  Brushless DC ( BLDC ) Motor Fundamentals , 2019 .

[7]  Mauro Birattari,et al.  Swarm Intelligence , 2012, Lecture Notes in Computer Science.

[8]  Yiannis N Kaznessis,et al.  Optimization of a stochastically simulated gene network model via simulated annealing. , 2006, Biophysical journal.

[9]  Min-Sen Chiu,et al.  Robust PID controller design via LMI approach , 2002 .

[10]  Nazar Zaki,et al.  Prediction of protein inter-domain linkers using compositional index and simulated annealing , 2013, GECCO '13 Companion.

[11]  Kou-Yuan Huang,et al.  Very fast simulated annealing for pattern detection and seismic applications , 2011, 2011 IEEE International Geoscience and Remote Sensing Symposium.

[12]  Dimitris Bertsimas,et al.  Robust optimization with simulated annealing , 2010, J. Glob. Optim..

[13]  Hisham M. Soliman,et al.  PID/PI tuning for minimal overshoot of permanent-magnet brushless DC motor drive using particle swarm optimization , 2007 .

[14]  Stochastic Relaxation , 2014, Computer Vision, A Reference Guide.

[15]  P. Pillay,et al.  Application characteristics of permanent magnet synchronous and brushless DC motors for servo drives , 1991 .

[16]  Miguel A. Vega-Rodríguez,et al.  Simulated Annealing for Real-Time Vertical-Handoff in Wireless Networks , 2013, IWANN.

[17]  Lazhar Ben-Brahim,et al.  A fully digitized field-oriented controlled induction motor drive using only current sensors , 1992, IEEE Trans. Ind. Electron..

[18]  Sheldon Howard Jacobson,et al.  The Theory and Practice of Simulated Annealing , 2003, Handbook of Metaheuristics.

[19]  R. Krishnan,et al.  Permanent Magnet Synchronous and Brushless DC Motor Drives , 2009 .

[20]  Paul C. Krause,et al.  Analysis of electric machinery , 1987 .

[21]  Miguel A. Vega-Rodríguez,et al.  Evaluation of Different Metaheuristics Solving the RND Problem , 2009, EvoWorkshops.

[22]  Narayanaswamy Srinivasan,et al.  Understanding the role of domain–domain linkers in the spatial orientation of domains in multi-domain proteins , 2013, Journal of biomolecular structure & dynamics.

[23]  Chris Murphy,et al.  Dominance-Based Multiobjective Simulated Annealing , 2008, IEEE Transactions on Evolutionary Computation.

[24]  Nazar Zaki,et al.  Inter-domain linker prediction using amino acid compositional index , 2015, Comput. Biol. Chem..

[25]  Neil Genzlinger.  A. and Q , 2006 .

[26]  M. Kamel,et al.  A Taxonomy of Cooperative Search Algorithms , 2005, Hybrid Metaheuristics.

[27]  M.A. Rahman,et al.  Permanent magnet motors for brushless operation , 1988, Conference Record of the 1988 IEEE Industry Applications Society Annual Meeting.

[28]  M. Morari,et al.  Internal model control: PID controller design , 1986 .

[29]  J. G. Ziegler,et al.  Optimum Settings for Automatic Controllers , 1942, Journal of Fluids Engineering.

[30]  Michael Potter.  Facts , 2014, The Rise of Analytic Philosophy 1879–1930.

[31]  Lester Ingber,et al.  Simulated annealing: Practice versus theory , 1993 .

[32]  Sriyankar Acharyya,et al.  Comparative Performance of Simulated Annealing and Genetic Algorithm in Solving Nurse Scheduling Problem , 2008 .

[33]  Tong Heng Lee,et al.  On the design of multivariable PID controllers via LMI approach , 2002, Autom..