Global learning controls for uncertain relative degree one linear systems: a comparative study

The output tracking problem of periodic reference signals (of known period) for single-input single-output observable minimum phase uncertain linear time-invariant systems with unitary relative degree is considered. A continuous global iterative learning control via output error feedback is designed which guarantees closed loop boundedness and asymptotic output tracking, thus improving the L₂ convergence achieved. Its closed loop performances are then compared with those obtained by using the adaptive learning control and the adaptive regulator: in particular the effects of finite memory implementation, measurement noise and uncertainties in the period of the output reference signal are explicitly taken into account in the simulations.