  This work proposes a switched model reference adaptive control (S-MRAC)
architecture for a multi-input multi-output (MIMO) switched linear system with
memory for enhanced learning. A salient feature of the proposed method that
separates it from most previous results is the use of memory that store the
estimator states at switching and facilitate parameter learning during both
active and inactive phases of a subsystem, thereby improving the tracking
performance of the overall switched system. Specifically, the learning
experience from the previous active duration of a subsystem is retained in the
memory and reused when the subsystem is inactive and when the subsystem becomes
active again. Parameter convergence is shown based on an intermittent initial
excitation (IIE), which is significantly relaxed than the classical persistence
of excitation (PE) condition. A common Lyapunov function is considered to
ensure closed-loop stability with S-MRAC. Further under IIE, the exponential
stability of tracking and parameter estimation error dynamics are guaranteed.
