  In this paper we develop a multiple model reference adaptive controller
(MMRAC) with blending. The systems under consideration are non-square, i.e.,
the number of inputs is not equal to the number of states; multi-input, linear,
time-invariant with uncertain parameters that lie inside of a known, compact,
and convex set. Moreover, the full state of the plant is available for
feedback. A multiple model online identification scheme for the plant's state
and input matrices is developed that guarantees the estimated parameters
converge to the underlying plant model under the assumption of persistence of
excitation. Using an exact matching condition, the parameter estimates are used
in a control law such that the plant's states asymptotically track the
reference signal generated by a state-space model reference. The control
architecture is proven to provide boundedness of all closed-loop signals and to
asymptotically drive the state tracking error to zero. Numerical simulations
illustrate the stability and efficacy of the proposed MMRAC scheme.
