Adaptive Neuro-Predictive Position/Velocity Control of Robot Manipulators in Work Space

This paper proposes an adaptive Nonlinear Model Predictive Controller (NMPC) for hybrid position/velocity control of robot manipulators. Robot dynamics have generally uncertainties, including parameters variations, unknown nonlinearities of the robot, payload variations, and torque disturbances form the environment. The cost function of the NMPC is defined in such a way that by adjusting its weighting parameters, the end-effector of the robot tracks a predefined geometry path in Cartesian space with a constant velocity. Moreover, to eliminate the uncertainties, a neural network with Levenberg-Marquardt training algorithm is used to estimate adaptively the model of the robot. The closed-loop stability is demonstrated using Lyapunov theory. The validity of the proposed control method is shown by simulation results on a 3-DOF robot manipulator actuated by DC servomotors.