  Adaptive control can be applied to robotic systems with parameter
uncertainties, but improving its performance is usually difficult, especially
under discontinuous friction. Inspired by the human motor learning control
mechanism, an adaptive learning control approach is proposed for a broad class
of robotic systems with discontinuous friction, where a composite error
learning technique that exploits data memory is employed to enhance parameter
estimation. Compared with the classical feedback error learning control, the
proposed approach can achieve superior transient and steady-state tracking
without high-gain feedback and persistent excitation at the cost of extra
computational burden and memory usage. The performance improvement of the
proposed approach has been verified by experiments based on a DENSO industrial
robot.
