Commande Neurofloue Robuste des Systèmes Non-Linéaires

Abstract

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.Therefore, conventional controllers are not able to control movements under different distance, velocity, weight and the length of the arm manipulator. Consequently, for most conventional controllers, the training must be repeated each time where a new trajectory is encountered.The objective of this work 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, and then a larger learning space can be covered and the learning process is performed only once.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 work, a more exact method was used, represented by the Fourth-Order Runge-Kutta (RK-4) 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.