Model-Free Adaptive Control Algorithm with Data Dropout Compensation

Abstract EN

The convergence of model-free adaptive control (MFAC) algorithm can be guaranteed when the system is subject to measurement data dropout.

The system output convergent speed gets slower as dropout rate increases.

This paper proposes a MFAC algorithm with data compensation.

The missing data is first estimated using the dynamical linearization method, and then the estimated value is introduced to update control input.

The convergence analysis of the proposed MFAC algorithm is given, and the effectiveness is also validated by simulations.

It is shown that the proposed algorithm can compensate the effect of the data dropout, and the better output performance can be obtained.