Neuro-Terminal Sliding Control in arm movement using online routing algorithm

To control 3-DOF model of human arm movement in page and to reach robust control in external disturbance, unmodeled dynamics and uncertainties of model with time-varying properties, continues terminal sliding mode control as an adaptive-robust control was used. This controller have exponential convergence to zero tracing error, but chattering phenomenon in sliding control isn’t decrease desirable. In this paper, to decrease chattering, we coupled a recurrent neural network by a single hidden layer into the terminal sliding control (TSM). Moreover, because of systematic redundancy in the model of arm, don’t exist unique joint trajectories to considering as a default, so to reach online desired trajectories in reaching, online routing algorithm was used with Neuro-TSM control. For testing the robustness in control, we applied disturbance signals of torque. The results have shown,Neuro-TSM along with online routing algorithm, in addition to reducing chattering, could track joint trajectories and end effector path with very low errors.