Multiobjective approach developed for optimizing the dynamic behavior of incremental linear actuators. Issue 3 (29th April 2014)

• Record Type: Journal Article
• Authors: Amdouni, Imen; El Amraoui, Lilia; Gillon, Frédéric; Benrejeb, Mohamed; Brochet, Pascal
• Editors: Luc Dupré and Dr Guillaume Crevecoeur, Professor
• Is Part Of: Compel. Volume 33:Issue 3(2014)
• Page Start: 953
• Page End: 964
• Publication Date: 2014-04-29
• Subjects: Control systems -- Computer-aided design -- Electrical machines -- Multiobjective optimization -- FE method -- Parallel computing; Electrical engineering -- Data Processing -- Periodicals; Electrical engineering -- Mathematics -- Periodicals; Electrical engineering -- Periodicals; Electronics -- Data Processing -- Periodicals; Electronics -- Mathematics -- Periodicals; 621.3
• DOI: 10.1108/COMPEL-06-2013-0205
• Languages: English
• ISSNs: 0332-1649

Abstract

Abstract : Purpose: – The purpose of this paper is to develop an optimal approach for optimizing the dynamic behavior of incremental linear actuators. Design/methodology/approach: – 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. Finally, the multiobjective optimization approach is applied to the dynamic model to optimize control as well as design parameters. Findings: – The Pareto front resulting from the optimization approach (or the parallel optimization approach, ) is better than the Pareto, which is obtained from the only application of MultiObjective Genetic Algorithm (MOGA) method (or parallel MOGA with the same number of optimization approach objective function evaluations). The only use of MOGA can reach the region near an optimal Pareto front, but it consumes more computing time than the multiobjective optimization approach. At each flowchart stage, parallelization leads to a significant reduction of computing time which is halved when using two-core machine. Originality/value: – In order to solve the multiobjective problem, a hybrid algorithm based on MOGA is developed.