An effective distribution of flight control commands over many aircraft actuators (engines, control surfaces, flaps, etc.) can be achieved with constrained optimisation. Active-Set methods solve these problems efficiently, but their computational time requirements are still prohibitive for aircraft with many actuators or slower digital flight control processors. This work shows how these methods can be improved in these regards, by updating the required matrix factorisations at lower computational costs, rather than solving a separate optimisation problem at every step of the iterative algorithm. Additionally, it is shown how the sparsity of the problem matrices can be exploited. Both open-loop simulations and flight tests have been performed, which show that worst-case timings for a 6-rotor multicopter UAV can be improved by 65% over a current Active-Set solver. Furthermore, methods are presented that remedy numerical stability issues occurring in micro-controller floating point arithmetic but introduce a small but measurable adverse effect on the flight behaviour.