Intelligent hybrid control strategy for trajectory tracking of robot manipulators
Journal of Control Science and Engineering
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A Dual Neural Network for Kinematic Control of Redundant Manipulators Using Input Pattern Switching
Journal of Intelligent and Robotic Systems
Neural network-based nonlinear tracking control of kinematically redundant robot manipulators
Mathematical and Computer Modelling: An International Journal
Robotics and Autonomous Systems
Journal of Intelligent and Robotic Systems
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In this paper we propose a neural network adaptive controller to achieve end-effector tracking of redundant robot manipulators. The controller is designed in Cartesian space to overcome the problem of motion planning which is closely related to the inverse kinematics problem. The unknown model of the system is approximated by a decomposed structure neural network. Each neural network approximates a separate element of the dynamical model. These approximations are used to derive an adaptive stable control law. The parameter adaptation algorithm is derived from the stability study of the closed loop system using Lyapunov approach with intrinsic properties of robot manipulators. Two control strategies are considered. First, the aim of the controller is to achieve good tracking of the end-effector regardless the robot configurations. Second, the controller is improved using augmented space strategy to ensure minimum displacements of the joint positions of the robot. Simulation examples are also presented to verify the effectiveness of the proposed approach.