A Two–Time–Scale Neural Controller for the Tracking Control of Rigid Manipulators

In this paper, a two-time-scale neural controller applied to tile tracking control of rigid manipulators is introduced. Several fast learning rules and slow learning strategies are proposed. The stability properties of the closed loop system with the proposed two-time-scale neural controller are analyzed. The results show that the tracking error will be uniformly bounded and converge to a bounded region. If a sufficiently large leakage term is used in the fast learning rule, the ultimate bound of the tracking error depends only on the accuracy of the slow learning. Moreover, the feasibility of the proposed neural controller is demonstrated through the simulation of a two-link rigid robot manipulator.