Frequency selective learning model reference adaptive control

This study proposes a new frequency selective learning adaptive control approach for model reference adaptive control systems to improve their adaptation performance without depending on high learning rates. It is developed by inspiration from the philosophies of the Q-modification and concurrent learning approaches and retains the key advantages of both. The philosophy of this proposed approach is that the known part of the plant dynamics passes through multiple filters. By constraint of the governing dynamic equations, this result equals an expression containing the unknown parameters in a filtered version, which is used for augmentation of the update law. The use of multiple filters aims at increasing the rank of the update law based on online instantaneous information and for a sufficient number of filters at different bandwidths, exponential stability can be achieved in the presence of the structured matched uncertainty. Moreover, it can cope with a sudden change of the system configuration. As a result, it leads to an efficient adaptive control approach. Furthermore, a challenging roll control problem of an aircraft demonstrates the usefulness of this proposed approach.