Full order neural velocity and acceleration observer for a general 6-6 Stewart platform

The aim of this work is to combine different innovative methods to solve the forward kinematics (FK) problem in a parallel manipulator called Stewart platform. It leads to the solution of a set of simultaneous non-linear equations and results in a series of non-unique multiple sets of solutions. Many efforts have been made to solve this challenging problem and usually results in having to find the solution of a 16^th order polynomial by means of numerical methods such as Hooks-Jeeves, Steepest descent search and Newton-Raphson method (NR). Accuracy, speed and convergence of these methods are fully dependent to the initial guess vector that is fed to the numerical algorithm. In this paper, a simple feed forward network has been trained to calculate the approximate position of the mobile platform as the initial input vector. This vector is fed to the tuning package to acquire the precise solution of the FK problem. Two methods have been used to optimize the solution and the results have been compared. At the end, the optimized FK solution was applied in a numerical algorithm to calculate payload´s velocity and acceleration.