Adaptive Neural Network Tracking Control for Manipulators with Uncertainties

Abstract: An adaptive neural network controller is proposed to deal with the end-effector tracking problem of manipulators with uncertainties. By employing the adaptive Jacobian scheme, neural networks, and backstepping technique, the torque controller can be obtained which is demonstrated to be stable by the Lyapunov approach. The updating laws for designed controller parameters are derived by the projection method, and the tracking error can be reduced as small as possible. The favorable features of the proposed controller lie in that: (1) the uncertainty in manipulator kinematics is taken into account; (2) the “linearity-in-parameters” assumption for the uncertain terms in dynamics of manipulators is no longer necessary; (3) effects of external disturbances are considered in the controller design. Finally, the satisfactory performance of the proposed approach is illustrated by simulation results on a PUMA 560 robot.