A Global Adaptive Learning Control for Robotic Manipulators

This paper addresses the problem of designing a global adaptive learning control for robotic manipulators with revolute joints and unknown dynamics. The reference signals to be tracked are assumed to be smooth and periodic with known period. By developing in Fourier series expansion the input reference signals of every joint, an adaptive learning PD control is designed which 'learns’ the input reference signals by identifying their Fourier coefficients: global asymptotic tracking and local exponential tracking of both the input and the output reference signals is obtained when the Fourier series expansion of each input reference signal is finite, while arbitrary small tracking errors are achieved otherwise. The resulting control is not model based and depends only on the period of the reference signals and on some constant bounds on the robot dynamics.

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