Title: Fuzzy improved adaptive neuro-NMPC for online path tracking and obstacle avoidance of redundant robotic manipulators
Authors: Ashkan M.Z. Jasour, Mohammad Farrokhi
Addresses: Department of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran. ' Department of Electrical Engineering, Center of Excellence for Power System Automation and Operation, Iran University of Science and Technology, Tehran, Iran
Abstract: This paper presents a non-linear 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. Non-linear dynamic of the robot, including actuators dynamic, is also considered. Moreover, the online 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 show effectiveness of the proposed method.
Keywords: robotic manipulators; robot path tracking; obstacle avoidance; model predictive control; nonlinear MPC; fuzzy logic; adaptive control; neural networks; redundant robots; robot tracking; nonlinear dynamics; DC servomotors; robot actuators; robot control.
International Journal of Automation and Control, 2010 Vol.4 No.2, pp.177 - 200
Published online: 06 Jan 2010 *Full-text access for editors Access for subscribers Purchase this article Comment on this article