Optimization Design of a Spatial Six-Degree-of-Freedom Parallel Manipulator Based on Genetic Algorithms and Neural Networks

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.