MULTIVARIABLE MODEL-REFERENCE ADAPTIVE-CONTROL WITHOUT CONSTRAINTS ON THE HIGH-FREQUENCY GAIN MATRIX

A multivariable model reference adaptive control algorithm is presented for the case when the high-frequency gain matrix is unknown. Only an upper bound on the norm of the matrix needs to be known a priori. A transformation of the parameters, with a sort of hysteresis, is used to guarantee that a controller matrix, which is normally the inverse of the estimate of the high-frequency gain matrix, remains nonsingular. It is shown that all the signals in the adaptive system are bounded and that the tracking error and the regressor error converge to zero for all bounded reference inputs. Furthermore, exponential convergence is achieved when the regressor vector is persistently exciting.