Stable neural network adaptive control of constrained redundant robot manipulators

The paper deals with a neural network adaptive controller designed for constrained redundant robot manipulators. The controller has been determined using extended cartesian space to ensure minimum joint positions of the robot and to take into account mechanical constraints like joint limitations. The proposed approach guarantees a good minimization of any performance criterion, subject to either equality or inequality constraints while achieving the end-effector task. It verifies the repeatability property for closed or cyclic trajectories and avoids the computation of the inverse or pseudoinverse Jacobian extended matrix. Several neural networks are used to approximate separately the elements of the dynamical model of the robot manipulator written in cartesian space. Adaptation laws are derived for each network to ensure stability of the closed loop system. Simulations results demonstrate a good performance of the proposed controller.