The paper looks into Model Reference Adaptive Control (MRAC) based on a linear plant model with constant or slowly varying parameters. The actual plant is non-linear, of a higher model order, subjected to time-varying bounded disturbances, and the measured values may be corrupted by noise. These problems are explored and the adaptive algorithms are modified to counteract instability mechanisms and for improved robustness with respect to bounded disturbances and non-modeled dynamics. The adaptive controller identifies the dominant dynamics and uses feedforward to provide anticipative actions in tracing task while an adaptive feedback part stabilizes the tracking error dynamics. Also the effects of non-modeled high frequency dynamics and bounded disturbances on stability and performance are analyzed. The adaptive control scheme is robust in the sense that it guarantees the existence of a large region of attraction from which all the trajectories remain bounded. The size of the region of attraction depends on the non-modeled dynamics in such a way that if the non-modeled dynamics is infinitely fast, the region of attraction becomes the whole space. Simulation and experimental results are presented and discussed to demonstrate the strength of the proposed algorithm.

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