Continuous-time neural control for a 2 DOF vertical robot manipulator

Abstract

This paper presents a continuous-time neural control scheme for identification and control of a two degrees of freedom (DOF) direct drive vertical robot manipulator model, on which effects due to friction and gravitational forces are both considered. A recurrent high-order neural network (RHONN) structure is proposed in order to identify the plant model to then, based on this neural structure, derive a neural controller using the backstepping design methodology. The trajectory tracking performance of the neural controller is illustrated via simulations results, which suggest the validity of the proposed approach for its implementation in real-time.