Robust neural-fuzzy-network control for rigid-unk electrically driven robot manipulator

Abstract

This study addresses the design and analysis of an intelligent control system for an n-link robot manipulator to achieve the high-precision position tracking. According to the concepts of mechanical geometry and motion dynamics, the dynamic model of an n-link robot manipulator including actuator dynamics is introduced initially. However, it is difficult to design a suitable model-based control scheme due to the uncertainties in practical applications, such as friction forces, external disturbances and parameter variations. In order to deal with the mentioned difficulties, a robust neural-fuzzy-network control (RNFNC) system is investigated to the joint position control of an n-link robot manipulator for periodic motion. In this control scheme, a four-layer neural-fuzzy-network (NFN) is utilized for the major control role, and the adaptive tuning laws of network parameters are derived in the sense of projection algorithm and Lyapunov stability theorem to ensure the network convergence as well as stable control performance. The merits of this model-free control scheme are that not only the stable position tracking performance can be guaranteed, but also no prior system information and auxiliary control design are required in the control process. In addition, numerical simulations of a two-link robot manipulator actuated by DC servomotors are provided to verify the effectiveness and robustness of the proposed RNFNC methodology.