  This work proposes an online adaptive identification method for multi-input
multi-output (MIMO) switched affine systems with guaranteed parameter
convergence. A family of online parameter estimators is used that is equipped
with a dual-layer low pass filter architecture to facilitate parameter learning
and identification of each subsystem. The filters capture information about the
unknown parameters in the form of a prediction error which is used in the
parameter estimation algorithm. A salient feature of the proposed method that
distinguishes it from most previous results is the use of a memory bank that
stores filter values and promotes parameter learning during both active and
inactive phases of a subsystem. Specifically, the learnt experience from the
previous active phase of a subsystem is retained in the memory and leveraged
for parameter learning in its subsequent active and inactive phases. Further, a
new notion of intermittent initial excitation (IIE) is introduced that extends
the previously established initial excitation (IE) condition to the switched
system framework. IIE is shown to be sufficient to ensure exponential
convergence of the switched system parameters.
