Forward kinematics of the Stewart platform using genetic neural algorithm

The Stewart platform's unique structure presents an interesting problem in its forward kinematics solution. It involves the solving of a series of simultaneous non-linear equation and, usually, non-unique, multiple sets of solutions are obtained from one set of data. The genetic-neural learning algorithm is guided by the fitness function of population, rather than gradient direction. A multiplayer genetic-neural network is trained to recognize the relationship between the input values and the output values of the forward kinematics of the Stewart platform. The result shows good optimization, the ratio of reduction of is least or less than 53%. The most important result of the implemented algorithm is the reduction of time required to train the neural network.