Research on Determination of Optimum Parameters for The Lower Extremity Exoskeleton

International Conference on Recent Advances in Industrial Engineering and Manufacturing

工业技术（总论）

Tho, N.H.^1 ; Vu, T.T.T.^2 ; Nghi, H.H.^2

Faculty of Mechanical Engineering and Technology, Ho Chi Minh City University of Food Industry (HUFI), 140 Le Trong Tan St., Tan Phu Dist., Ho Chi Minh City

700000, Viet Nam^1

Ho Chi Minh City University of Technology, VNU-HCM, 268 Ly Thuong Kiet St., Dist. 10, Ho Chi Minh City

700000, Viet Nam^2

关键词: Alternative solutions;    Boxbehnken design (BBD);    Evolutionary algorithms (EAs);    Lower extremity exoskeletons;    Multiple-objective optimization;    Non-dominated sorting genetic algorithm;    ii;    Pareto-optimal frontiers;    Response surface methodology;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/530/1/012013/pdf
DOI  :  10.1088/1757-899X/530/1/012013

学科分类：工业工程学

来源: IOP

【 摘 要 】

Lower extremity exoskeleton is a device that enables the user to sit down without the need of using a chair. The design of the exoskeleton has to satisfy multiple objectives, many of which are defined by the body of the user. Most CAD (Computer aided design) programs nowadays still have not added multiple-objective evolutionary algorithms (EAs). This paper presents an application of NSGA-II (Non-dominated sorting genetic algorithm II) and AMO (Adaptive-Multiple objectives) in order to solve the problem of multiple-objective optimization in designing Lower extremity exoskeleton. Moreover, the Box-Behnken design (BBD) - based response surface methodology was also used to investigate the effects of the number of initial samples, the number of samples per iteration and the maximum number of iterations on AMO. Then, the Pareto-optimal frontier of feasible points was carried out for the alternative solutions of design problem. In addition, the TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method was employed in order to support the decision makers in deciding the engineering optimum parameters. The optimum parameters obtained are αmin = 24.81°, αmax=1 15.08° and (Wd = 177.2°. With the same input values, AMO and NSGA-II gave the same output values, however the number of samples which are used to determine convergence of AMO only equals to 0.66 that of NSGA-II. The method in this study is advantageous for multiple-objective optimization in CAD problems.