Robust Neural-Fuzzy-Network Control for Robot Manipulator Including Actuator Dynamics

This paper 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 a projection algorithm and the Lyapunov stability theorem to ensure network convergence as well as stable control performance. The merits of this model-free control scheme are that not only can the stable position tracking performance be guaranteed but also no prior system information and auxiliary control design are required in the control process. In addition, numerical simulations and experimental results of a two-link robot manipulator actuated by dc servo motors are provided to verify the effectiveness and robustness of the proposed RNFNC methodology.