Experimental Results With the Symbiotic Control Framework on an Aerospace Platform

Fixed-gain control and adaptive learning architectures aim to mitigate the effects of uncertainties. In particular, fixed-gain control offers a more predictable closedloop system behavior, but requires the knowledge of uncertainty bounds. On the other hand, while adaptive learning does not necessarily require such knowledge, it can result in a less predictable closed-loop system behavior compared to the previous one. To this end, a novel symbiotic control framework has been recently introduced, which unifies fixed-gain control and adaptive learning architectures to suppress the adverse effects of system uncertainties more predictably than adaptive learning alone, offering the strength of both fixed-gain and adaptive architectures. The contribution of this paper is to provide an application of symbiotic control framework to an aerospace platform for the purpose of illustrating the stability and performance of this framework in an experiment that includes the presence of unmodeled uncertainties, making the system more difficult to control.