Successive and Parallel Optimization of Linear Actuator Behaviors

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Throughout this paper a magnetostatic and a dynamic model of an incremental linear actuator are implemented in the goal to improve the static force and the overflow of the dynamic response over two successive step displacements by optimizing its design and control parameters. First a parameterized design model is built. Second, a dynamic model is implemented. This model takes into account the thrust force computed from a Finite Element model. Third, a successive optimization of design and control parameters of the incremental actuator is applied using two hybrid monoobjective algorithms implemented under the elaborated platform. Finally, a parallel optimization of control and design parameters of the studied actuator is performed using monoobjective and multiobjective algorithm developed under the OPtimization Platform (O2P).
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Control; Design; Dynamic Simulation; Magnetostatic; Model; Monoobjective; Multiobjective; Optimization

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S. Brisset, Approaches and Tools for Optimal Design of Electrical Machines, Empowerment to supervise research, April 2008.

T.V. TRAN, Problèmes Combinatoires et Modèles Multi-Niveaux pour la Conception Optimale des Machines Électriques, Ph.D. dissertation, Ecole Centrale de Lille, France, 2009.

F.M. Messad, Méthodologie et algorithmes adaptés à l'optimisation multi-niveaux et multi-objectif de systèmes complexes, Ph.D. dissertation, Ecole Centrale de Lille, France, 2009.

A. Berro, Optimisation multiobjectif et stratégie d’évolution en environnement dynamique, Ph.D. dissertation, Université des Sciences Sociales Toulouse, France, 2001.

X. D. Xue, K.W. E. Cheng, T.W. Ng and N. C. Cheung, Multi-Objective Optimization Design of In-Wheel Switched Reluctance Motors in Electric Vehicles, IEEE Transactions on Industrial Electronics, Vol. 57, N°. 9, September 2010.

H.A. Taboada, J.F. Espiritu, and D.W. Coit, MOMS-GA: A Multi-Objective Multi-State Genetic Algorithm for System Reliability Optimization Design Problems, IEEE Transactions on Reliability, Vol. 57, N°. 1, March 2008.

S. Brisset, P. Brochet, Optimization of Switched Reluctance Motors Using Deterministic Methods with Static and Dynamic Finite Element Simulations, IEEE Transactions on Magnetic, Vol. 34, N°. 5, part I of two parts, pp. 2853-2856, September 1998.

C. M. Fonseca and P. J. Fleming, multiobjective Genetic Algorithms, IEE Colloquium on Genetic Algorithms for Control Systems Engineering, September 1993.

T.V. TRAN, Combinatorial problems and multi-level models for optimal design of electrical machines, Ph.D. dissertation, Ecole Centrale de Lille, France, 2009.

F.M. Messad, Methodology and algorithms adapted to the multi-level and multiobjective optimization of complex systems, Ph.D. dissertation, Ecole Centrale de Lille, France, 2009.

J.W. Herrmann, A Genetic Algorithm for Minimax Optimization Problems, Proceedings of the Congress on Evolutionary Computation, 1999.

K. Deb,T. Goel, A hybrid multi-objective evolutionary approach to engineering shape design, Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization (EMO-01), 2001. pp. 385-399.

K. Sindhya, K. Deb, K. Miettinen, A local search based evolutionary multi-objective optimization technique for fast and accurate convergence, Proceedings of the Parallel Problem Solving From Nature (PPSN-2008), Berlin, Germany: Springer-Verlag; 2008.

J. Simkin and C. W. Trowbridge, Optimizing Electromagnetic Devices Combining Direct Search Methods with Simulated Annealing, IEEE Trans. Magn. , Vol. 28, No. 2, March 1992.

G. Drago, A. Manella, M. Nervi, M. Repetto and G. Secondo, A Combined Strategy for Optimization in Non Linear Magnetic Problems Using Simulated Annealing and Search Techniques, IEEE Trans. Mag . , Vol. 28, No. 2, March 1992.

F. Neri, X. del Toro Garcia, G. L. Cascella, N. Salvatore, Surrogate Assisted Local Search on PMSM Drive Design, COMPEL: International Journal for Computation and Mathematics in Electrical and Electronic Engineering, vol. 27, pp. 573-592, 2008.

L. Benasla, A. Belmadani and M. Rahli, Hooke-Jeeves Method Applied to a New Economic Dispatch Problem Formulation, Journal of Information Science and Engineering, vol. 24, pp. 907-917, 2008.

D. C. Montgomery, Design and Analysis of Experiments, 6th ed. Hoboken, New Jersey: John Wiley and Sons, 2005.

I. Amdouni, R. Saadaoui, L. El Amraoui, F. Gillon, M. Benrejeb and P. Brochet, Kernel software platform for electrical devices optimization: application to linear actuator performance’s optimization, International Journal of Applied Electromagnetics and Mechanics Vol. 37, N°. 7, pp. 147-157, 2011.

S. Vujević, I. Jurić-Grgić, R. Lucić, Time-Harmonic and Transient Linear Circuit Analysis Using Finite Element Technique, (2008) International Review on Modelling and Simulations (IREMOS), 1 (2), pp. 275-280.

Bach, T., Yao, J., The simulated annealing Q-learning application to the dynamic power markets, (2012) International Review on Modelling and Simulations (IREMOS), 5 (5), pp. 2321-2327.

S. Meo, Vectorial Control of Linear Induction Machines Taking into Account End-Effects, (2012) International Review on Modelling and Simulations (IREMOS), 5 (3), pp. 1210-1215.

S. Meo, A. Ometto, N. Rotondale, Impact of poles Number and Windings on the Performance of Linear Induction Motors, (2012) International Review of Electrical Engineering (IREE), 7 (3), pp. 4352-4358.


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