Optimal Forward Kinematics Modeling of Stewart Manipulator Using Genetic Algorithms

In this paper, a new technique for modeling the forward kinematics of a Stewart manipulator is presented based on a predicted squared error cost function. Initially, the inverse kinematics model was introduced in detail for seeding the regression points. Genetic algorithms were then employed to search for optimal model structure from a set of candidate regression predictors. The proposed technique provides a significant reduction in the expenses of memory and computation compared to the traditional Taylor series expansion model. Such reduction increases the suitability of the model for a task space control application of the Stewart manipulator