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

MRAC with Memory for Switched Linear Systems