Task-space control for industrial robot manipulators with unknown inner loop control architecture

The operational space control of a robot manipulator using external sensors requires stabilizing the compound system {external sensors - outer controller - inner controller - robot manipulator}. The user must access the inner controller to reshape it to achieve this stabilization. Due to intellectual property protection purposes, most industrial robots have an unknown or inaccessible inner controller. Therefore, it is tricky to design a stable control scheme. To solve this problem, an adaptive radial basis function neural network (RBF NN) outer controller is proposed, which approximates the inner controller’s dynamics to eliminate its effect in the closed-loop. An inherent property for RBF NN is used to reduce the number of adaptive parameters. Since this technique introduces approximation errors, it is included in the control scheme, a term that constrains the system to converge rapidly to the performances prescribed by the user. It is proved that all the closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB) through Lyapunov theory. The effectiveness of the proposed approach is verified through simulation comparisons and experimental studies.