Fuzzy improved adaptive neuro-NMPC for online path tracking and obstacle avoidance of redundant robotic manipulators

This paper presents a Nonlinear Model Predictive Control (NMPC) for redundant robotic manipulators. Using NMPC, the end-effector of the robotic manipulator tracks a predefined geometry path in Cartesian space in such a way that no collision with obstacles in the workspace and no singular configurations for the robot occurs. Nonlinear dynamic of the robot, including actuators dynamic, is also considered. Moreover, the on-line tuning of the weights in NMPC is performed using the fuzzy logic. The proposed method automatically adjusts the weights in the cost function in order to obtain good performance. Furthermore, using neural networks for model prediction, no prior knowledge about system parameters is necessary and system robustness against changes in its parameters is achieved. Numerical simulations of a 4DOF redundant spatial manipulator actuated by DC servomotors shows effectiveness of the proposed method.