Adaptive RBFNN type-2 fuzzy sliding mode controller for robot arm with pneumatic muscles

In this paper, we aim to propose a new robust controller for robot arm driven by pneumatic muscles. Based on sliding mode theory, this control approach consists on the combination of radial based function neural network and type-2 fuzzy logic system. First, the control approach was presented and the stability of the system in closed loop was analyzed using Lyapunov stability theorem. Next, the joints of 2-DOF manipulator robot were approximated as differential linear equations with parameters uncertainties and simulations were given to proof the efficiency and the superiority of this approach compared to radial based function network type-1 fuzzy sliding mode controller used as reference. Last, experimental validation of the proposed approach was presented.