Design of Intelligent Hybrid Force and Position Control of Robot Manipulator

This work considers the Hybrid Force/Position control of robot manipulator in the presence of uncertainties and external disturbances. The proposed controller contains the model based term, Radial Basis Function neural network term plus an adaptive bound part. The Radial basis function neural network is functioning to learn a non linear function with no requirement of off line training. An adaptive bound part is developed to guess the unknown bound on the unmodeled disturbance, neural network reconstruction error and friction term. The Lyapunov function approach is used to the stability of the system. In the end simulations results are presented for two link robot manipulators.