Robust adaptive regulation of manipulators: theory end experiments

This paper considers the position regulation problem for uncertain robot manipulators and proposes two adaptive controllers as solutions to this problem. The first controller is derived under the assumption that the manipulator state is measurable, while the second strategy is developed for those applications in which only position measurements are available. Each of the adaptive schemes is computationally simple, requires very little information regarding the manipulator model or the payload, and ensures that the position error possesses desirable convergence properties: semiglobal asymptotic convergence if no external disturbances are present and semiglobal convergence to an arbitrarily small neighbourhood of zero in the presence of bounded disturbances. Experimental results are presented for a Zebra Zero manipulator and demonstrate that accurate and robust position control can be achieved by using the proposed controllers