This paper presents a learning cerebellar model to control reaching movements of a simulated biomimetic manipulator.Utilizing the servo mechanism of the spinal reflex circuitry

the model embeds a neural network based on known cerebellar circuitry in a simulation of the mammalian motor control system to control a 6-muscle 2-link planar arm.The system had implemented a biologically version of the parallel hierarchical control model proposed by Katayama and Kawato and was proved to be able to learn accurate trajectory control.The simulation results demonstrate that this cerebellar model was able to learn parts of the inverse dynamics model not provided by the PDF＋F controller

and having nicer tracing performance of desired trajectory.