Control of a direct drive robot using fuzzy spiking neural networks with variable structure systems-based learning algorithm

Abstract

In this work, a sliding mode theory based supervised training algorithm that implements fuzzy reasoning on a spiking neural network has been developed and tested on the trajectory control problem of a two-degrees-of-freedom direct drive robotic manipulator. To describe the generation of a new spike train from the incoming spike trains Spike Response Model has been utilized and the Lyapunov stability method has been adopted in the derivation of the update rules for the neurocontroller parameters. The results of the real-time experiments indicate that stable online tuning and fast learning speed are the prominent characteristics of the proposed algorithm.