An adaptive neural control scheme for mechanical manipulators with guaranteed stability

An adaptive neural control scheme for mechanical manipulators is presented. The design basically consists of a neural controller which implements a feedback linearization control law for a generic manipulator with unknown parameters, and a sliding-mode control which improves the robustness of the design and compensates for the neural approximation errors. The resulting closed-loop system is stable and the trajectory-tracking control objective is asymptotically achieved