Data-driven dynamic optimal allocation for uncertain over-actuated linear systems

The dynamic control allocation problem for LTI systems is addressed in an uncertain setting. In the presence of unstructured uncertainties affecting the underlying plant, a completely data-driven strategy is envisioned to optimally allocate the control action in the presence of non-constant steady-state behavior of the plant, while leaving untouched the regulated output response induced by an a priori given controller. Compared with the current state of the art, the proposed solution exhibits several appealing features. Such features are: complete invisibility of the allocator's action (after a training interval if the plant is unknown), exact optimization of the periodic steady-state evolution, arbitrary speed of the allocation action; while all of them are achieved even for unknown plants in this paper, in the current literature they are impossible to achieve or just obtainable for a perfectly known plant.