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Repetitive learning control is presented for finite- time-trajectory tracking of uncertain time-varying robotic sys- tems.A hybrid learning scheme is given to cope with the con- stant and time-varying unknowns in system dynamics,where the time functions are learned in an iterative learning way,without the aid of Taylor expression,while the conventional differential learning method is suggested for estimating the constant ones. It is distinct that the presented repetitive learning control avoids the requirement for initial repositioning at the beginning of each cycle,and the time-varying unknowns are not necessary to be periodic.It is shown that with the adoption of hybrid learning, the boundedness of state variables of the closed-loop system is guaranteed and the tracking error is ensured to converge to zero as iteration increases.The effectiveness of the proposed scheme is demonstrated through numerical simulation.
Repetitive learning control is presented for finite-time-tracking tracking of uncertain time-varying robotic sys- tems. A hybrid learning scheme is given to cope with the con- stant and time-varying unknowns in system dynamics, where the time functions are learned in an iterative learning way, without the aid of Taylor expression, while the conventional differential learning method is suggested for estimating the constant one. It is distinct that the presented repetitive learning control avoids the requirement for initial repositioning at the beginning of each cycle, and the time-varying unknowns are not necessary to be periodic. It is shown with the adoption of hybrid learning, the boundedness of state variables of the closed-loop system is guaranteed and the tracking error is ensured to converge to zero as iteration increases. The effectiveness of the proposed scheme is demonstrated through numerical simulation.