Robust Adaptive Tracking Control of Manipulator Arms with Fuzzy Neural Networks

The learning space for executing general motions of a flexible joint manipulator is quite large and the dynamics are, in general, nonlinear, time-varying, and complex. The objective of this paper is to design a nonlinear system based on the fuzzy neural network control using supervised training, into executing reference trajectories by a flexible joint manipulator. The structure identifications of controller networks are performed by using the Adaptive Neural Fuzzy Inference System (ANFIS), with new parameters and weight coefficients automatically adapted and adjusted, in order to decrease position tracking errors. In order to adapt and reduce the number of undefined parameters in the network, a new technique is used. Reported research works use the Euler method for the resolution of the arm's dynamic function, in this paper, a more exact method was used, represented by the Fourth-Order Runge-Kutta (RK4) method. A comparative study has been carried out between these two methods in order to prove the effectiveness of the later. Finally, in order to test the robustness of the proposed approach, it was also investigated considering parameter variations. The tracking speed of the model on the system control accuracy was also analyzed. The simulation results show that the proposed approach has a good tracking effect