Introduction to Robotics: Mechanics and Control
Introduction to Robotics: Mechanics and Control
Control of Robot Manipulators
Adaptive friction compensation using neural network approximations
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Neural-network approximation of piecewise continuous functions: application to friction compensation
IEEE Transactions on Neural Networks
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In this paper, an adaptive control method for hybrid position/force control of robot manipulators, based on neuro-fuzzy modeling, is presented. Since force control involves applying certain amount of force on the surface of an object, it is important to consider the friction force between end-effector and surface into account. In order to compensate this friction force, a robust and adaptive neuro-fuzzy compensator will be designed and incorporated into the close-loop system. Moreover, to determine stiffness coefficient of surface, an on-line estimator will be designed for more precise computation of the desired force. Due to the adaptive neuro-fuzzy modeling, the proposed controller is independent of robot dynamics, since the free parameters of the neuro-fuzzy controller are adaptively updated to cope with changes in the system and the environment. As a result, the tracking error, both for position and force, will always remain small. Also, the stability of the controller is guaranteed, since the adaptation law is based on Lyapunov theory. In addition to that, the convergence of the adaptive parameters will be proved in this paper. The simulation results show good performance of the proposed controller as compared with other conventional control schemes for robot manipulators such as computed torque method.