Solving the forward kinematics problem of six-DOF Stewart platform using multi-task Gaussian process

Abstract

The forward kinematics problem of general six-degree-of-freedom Stewart platform is addressed in this article. Unlike the convention taking positional variables as independent ones and solving them individually, this article presents an alternative approach which takes the positional variables as multiple-related tasks and exploits the commonality between them using a multi-task Gaussian process learning method, as a result, a simple adaptive algorithm, which may satisfy the requirements for high accuracy and real-time processing, is established. Moreover, the proposed algorithm can achieve the desired accuracy using 1000 training samples at most, which is far less than those of other algorithms. Simulation results on a Stewart platform used in aircraft flight simulation system show that the proposed algorithm can achieve superior performance.