OPTIMIZATION DESIGN OF A SPATIAL SIX-DEGREE-OF-FREEDOM PARALLEL MANIPULATOR BASED ON GENETIC ALGORITHMS AND NEURAL NETWORKS

被引:0
|
作者
Zhang, Dan [1 ]
Gao, Zhen [1 ]
机构
[1] Univ Ontario, Inst Technol, Fac Engn & Appl Sci, Oshawa, ON L1H 7K4, Canada
来源
DETC 2008: PROCEEDINGS OF THE ASME INTERNATIONAL DESIGN ENGINEERING TECHNICAL CONFERENCES AND COMPUTERS AND INFORMATION IN ENGINEERING CONFERENCE, VOL 1, PTS A AND B: 34TH DESIGN AUTOMATION CONFERENCE | 2009年
关键词
OPTIMUM DESIGN; 3R MANIPULATORS;
D O I
暂无
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Optimizing the performances of parallel manipulators by adjusting the structure parameters can be a difficult and time-consuming exercise especially when the parameters are multifarious and the objective functions are too complex. Artificial intelligence approaches can be investigated as the effective criteria to address this issue. In this paper, genetic algorithms and artificial neural network are implemented as the intelligent optimization criteria of global stiffness and dexterity for spatial six degree-of-freedom (DOF) parallel manipulator. The objective functions of global stiffness and dexterity are calculated and deduced according to the kinetostatic model. Neural networks are utilized to model the solutions of performance indices. Multi-objective optimization is developed by Pareto-optimal solution. The effectiveness of the proposed methodology is proved by simulation.
引用
收藏
页码:767 / 775
页数:9
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