Adaptive Fuzzy-Neural-Network Control of Robot Manipulator Using T-S Fuzzy Model Design

This study focuses on the development of an adaptive fuzzy-neural-network control (AFNNC) scheme 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, an AFNNC system is investigated without the requirement of prior system information. In this model-free control scheme, a continuous-time Takagi-Sugeno (T-S) dynamic fuzzy model with on-tine learning ability is constructed for representing the system dynamics of an n-link robot manipulator. Then, a four-layer fuzzy-neural-network (FNN) is utilized for estimating nonlinear dynamic functions in this fuzzy model. Moreover, the AFNNC law and adaptive tuning algorithms for FNN weights are established in the sense of Lyapunov stability analyses to ensure the network convergence as well as 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 AFNNC methodology. In addition, the superiority of the proposed control scheme is indicated in comparison with proportional-differential control (PDC), Takagi-Sugeno-Kang (TSK) type fuzzy-neural-network control (T-FNNC), robust-neural-fuzzy-network control (RNFNC), and fuzzy-model-based control (FMBC) systems.