A Fast Adaptive Memetic Algorithm for Online and Offline Control Design of PMSM Drives

A fast adaptive memetic algorithm (FAMA) is proposed which is used to design the optimal control system for a permanent-magnet synchronous motor. The FAMA is a memetic algorithm with a dynamic parameter setting and two local searchers adaptively launched, either one by one or simultaneously, according to the necessities of the evolution. The FAMA has been tested for both offline and online optimization. The former is based on a simulation of the whole system-control system and plant-using a model obtained through identification tests. The online optimization is model free because each fitness evaluation consists of an experimental test on the real motor drive. The proposed algorithm has been compared with other optimization approaches, and a matching analysis has been carried out offline and online. Excellent results are obtained in terms of optimality, convergence, and algorithmic efficiency. Moreover, the FAMA has given very robust results in the presence of noise in the experimental system

[1]  M. Karplus,et al.  The topology of multidimensional potential energy surfaces: Theory and application to peptide structure and kinetics , 1997 .

[2]  David Naso,et al.  On-line genetic design of fuzzy controllers for DC drives with variable load , 2003 .

[3]  Prashanth Krishnamurthy,et al.  Modeling and Adaptive Nonlinear Control of Electric Motors , 2003 .

[4]  William E. Hart,et al.  Memetic Evolutionary Algorithms , 2005 .

[5]  Peter J. Fleming,et al.  Evolutionary algorithms in control systems engineering: a survey , 2002 .

[6]  R. K. Ursem.  Multi-objective Optimization using Evolutionary Algorithms , 2009 .

[7]  James G. Berryman,et al.  Iterative methods of optimization with application to crosswell tomography , 2007 .

[8]  David Naso,et al.  On-line genetic design of anti-windup unstructured controllers for electric drives with variable load , 2004, IEEE Transactions on Evolutionary Computation.

[9]  M. Sumner,et al.  On-line and off-line control design in power electronics and drives using genetic algorithms , 2004, Conference Record of the 2004 IEEE Industry Applications Conference, 2004. 39th IAS Annual Meeting..

[10]  Andy J. Keane,et al.  Meta-Lamarckian learning in memetic algorithms , 2004, IEEE Transactions on Evolutionary Computation.

[11]  John Yen,et al.  A hybrid approach to modeling metabolic systems using a genetic algorithm and simplex method , 1998, IEEE Trans. Syst. Man Cybern. Part B.

[12]  Lakhmi C. Jain,et al.  Intelligent Adaptive Control: Industrial Applications , 1998 .

[13]  R. Krishnan,et al.  Electric Motor Drives: Modeling, Analysis, and Control , 2001 .

[14]  Peter J. Fleming,et al.  On-line evolution of robust control systems: an industrial active magnetic bearing application , 2001 .

[15]  Robert Hooke,et al.  `` Direct Search'' Solution of Numerical and Statistical Problems , 1961, JACM.

[16]  Jim Smith,et al.  A Memetic Algorithm With Self-Adaptive Local Search: TSP as a case study , 2000, GECCO.

[17]  L. Darrell Whitley,et al.  The GENITOR Algorithm and Selection Pressure: Why Rank-Based Allocation of Reproductive Trials is Best , 1989, ICGA.

[18]  Karl Johan Åström,et al.  Adaptive Control (2 ed.) , 1995 .

[19]  Wander G. da Silva,et al.  Application of genetic algorithms to the online tuning of electric drive speed controllers , 2000, IEEE Trans. Ind. Electron..

[20]  K.J. Astrom,et al.  Design of PID controllers based on constrained optimization , 1999, Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251).

[21]  Kevin Kok Wai Wong,et al.  Classification of adaptive memetic algorithms: a comparative study , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[22]  Weiping Li,et al.  Applied Nonlinear Control , 1991 .

[23]  Kaisa Miettinen,et al.  Nonlinear multiobjective optimization , 1998, International series in operations research and management science.

[24]  Natalio Krasnogor,et al.  Towards Robust Memetic Algorithms , 2005 .

[25]  Hans Gross,et al.  Electrical Feed Drives in Automation: Basics, Computation, Dimensioning , 2001 .

[26]  David B. Fogel,et al.  Tuning Evolutionary Programming for Conformationally Flexible Molecular Docking , 1996, Evolutionary Programming.

[27]  Zbigniew Michalewicz,et al.  GAVaPS-a genetic algorithm with varying population size , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[28]  N. Salvatore,et al.  On-line simplex-genetic algorithm for self-commissioning of electric drives , 2005, 2005 European Conference on Power Electronics and Applications.

[29]  Larry J. Eshelman,et al.  The CHC Adaptive Search Algorithm: How to Have Safe Search When Engaging in Nontraditional Genetic Recombination , 1990, FOGA.

[30]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[31]  J. Liao,et al.  A hybrid approach to modeling metabolic systems using genetic algorithm and simplex method , 1995, 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century.

[32]  Bill Drury,et al.  The Control Techniques Drives and Controls Handbook , 2009 .

[33]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[34]  Tore Hägglund,et al.  The future of PID control , 2000 .

[35]  Thomas Bäck,et al.  The Interaction of Mutation Rate, Selection, and Self-Adaptation Within a Genetic Algorithm , 1992, PPSN.

[36]  Kok Wai Wong,et al.  Choice of Memes In Memetic Algorithm , 2003 .

[37]  Raúl Hector Gallard,et al.  Hybrid evolutionary algorithms for the TSP , 2001 .

[38]  Hans-Paul Schwefel,et al.  Evolution and optimum seeking , 1995, Sixth-generation computer technology series.

[39]  Werner Leonhard,et al.  Control of Electrical Drives , 1990 .

[40]  Jim Smith,et al.  Operator and parameter adaptation in genetic algorithms , 1997, Soft Comput..