وحید میگلی

Industrial robots are manipulators with high precision and repeatability making them proper alternatives to humans. In common industrial robots proportional-Integral controllers are exploited owing to their simplicity; however, they cannot guarantee appropriate and robust operation. To obtain suitable performance nonlinear controller is recommended. Sliding mode controller is a state feedback controller with fast transient response which is robust against uncertainties. Despite its advantages sliding mode controller is not a good choice for steady states due to chattering phenomenon. On the other hand, adaptive neuro-fuzzy controllers have been successful, though they have not acceptable performance when encountering uncertainties. It is a consequence of inevitable training phase in such controllers. In this study a hybrid controller is proposed combining sliding mode and adaptive neuro-fuzzy controller with variable weights to take advantage of both structures. A fuzzy supervisor is tasked with optimal adjustment of the weights. Besides, it facilitates switching between two controllers.