The output feedback tracking control problem for
induction motor servo drives with mechanical uncertainties is
addressed. Under the assumption that the reference profile for the
rotor angle is periodic with known period, an adaptive learning
control is designed, which "learns" the non-structured unknown
periodic disturbance signal due to mechanical uncertainties by
identifying the Fourier coefficients of any truncated approximation,
while guaranteing L_2 and L_inf transient performances.
It is shown that, for any motor initial condition belonging
to an arbitrary given compact set: i) the guaranteed output
tracking precision improves by increasing the number of terms
in the truncated Fourier series; ii) when the unknown periodic
disturbance can be represented by a finite Fourier series, it
is exponentially reconstructed by the learning algorithm and
exponential output tracking is achieved. Simulation results for a
digital implementation of the proposed controller are provided
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