Adaptive fuzzy-neural-network velocity sensorless control for robot manipulator position tracking

This study focuses on the development of an adaptive fuzzy-neural-network velocity sensorless control (AFNNVSC) scheme for an n-link robot manipulator to achieve high-precision position tracking. In general, it is difficult to adopt a model-free design without the joint velocity/acceleration information to achieve this control objective owing to uncertainties in practical applications, such as friction forces, external disturbances and parameter variations. In order to cope with this problem, an AFNNVSC scheme including a non-linear observer and a fuzzy-neural-network (FNN) controller is investigated without the requirement of prior system information. This non-linear observer is used to estimate joint velocities of the robot manipulator. Then, a four-layer FNN is utilised for the major control role without auxiliary compensated control, and the adaptive tuning laws of network parameters are derived in the sense of projection algorithm and Lyapunov stability theorem to ensure the stable control performance. Experimental results of a two-link robot manipulator actuated by dc servomotors are given to verify the effectiveness and robustness of the proposed AFNNVSC methodology. In addition, the superiority of the proposed control scheme is indicated in comparison with the proportional-integral-differential control, computed torque control, Takagi-Sugeno-Kang-type fuzzy-neural-network control and robust-neural-fuzzy-network control systems.