Direct Adaptive Fuzzy-Neural-Network Control for Robot Manipulator by Using Only Position Measurements

This study focuses on the development of a direct adaptive fuzzy-neural-network control (DAFNNC) for an n-link robot manipulator to achieve high-precision position tracking. In general, it is difficult to adopt a model-based design to achieve this control objective due to the uncertainties in practical applications, such as friction forces, external disturbances and parameter variations. In order to cope with this problem, a DAFNNC strategy is investigated without the requirement of prior system information. In this model-free control topology, a FNN controller is directly designed to imitate a predetermined model-based stabilizing control law, and then the stable control performance can be achieved by only using joint position information. The DAFNNC law and the adaptive tuning algorithms for FNN weights are established in the sense of Lyapunov stability analyses to ensure the stable control performance. Numerical simulations of a two-link robot manipulator actuated by DC servomotors are given to verify the effectiveness and robustness of the proposed methodology. In addition, the superiority of the proposed control scheme is indicated in comparison with proportional-differential control (PDC), fuzzy-model-based control (FMBC), T-S type fuzzy-neural-network control (T-FNNC), and robust-neural-fuzzy-network control (RNFNC) systems.