A novel finite-time convergent estimation technique is proposed for identifying the amplitude, frequency
and phase of a biased sinusoidal signal. Resorting to Volterra integral operators with suitably designed
kernels, the measured signal is processed yielding a set of auxiliary signals in which the influence
of the unknown initial conditions is removed. A second-order sliding mode-based adaptation law –
fed by the aforementioned auxiliary signals – is designed for finite-time estimation of the frequency,
amplitude, and phase. The worst case behavior of the proposed algorithm in presence of the bounded
additive disturbances is fully characterized by Input-to-State Stability arguments. The effectiveness of
the estimation technique is evaluated and compared with other existing tools via extensive numerical
simulations.
