Data-Driven Dynamic Control Allocation for Uncertain Redundant Plants

This paper addresses the problem of achieving high-performance dynamic control allocation for uncertain plants by exploiting a data-driven design of the annihilator for the underlying plant. Previous work revealed that an output invisible control allocator can be decomposed as the cascade interconnection of a steady-state optimizer and an annihilator, where the latter unit modulates the allocator outputs in such a way to render such signals undetectable from the plant output. Clearly, the critical role and challenging requirements imposed on the annihilator make it the source of the fragility of control allocation schemes in the presence of uncertainty; nonetheless this critical aspect can be (almost) completely circumvented by tuning the annihilator to the actual plant parameters, namely by envisioning a data-driven control allocation scheme. Relations are also highlighted between the present results and the concepts of moments and orthogonal moments of a plant at frequencies of interest, whose use and estimation have recently been the subject of increasing interest.