Adaptive neural motion/force control of constrained robot manipulators by position measurement

In this paper, the Adaptive motion/force control problems of robot manipulators with uncertainties and end-effector constraints are addressed. A RBF neural networks and a linear observer are employed to construct the controller for constrained robot manipulators with only position measurement. The proposed controller guarantees that all the signals of the closed-loop system are bounded. The stability of the closed-loop system and the boundedness of tracking error are proved using Lyapunov stability synthesis. Finally, simulation results validate that the motion of the system converges to the desired trajectory, and the constraint force converges to the desired force.