Nonlinear dynamic inversion (NDI) is a nonlinear feedback linearization technique that has been widely applied to flight control systems [1,2]. Using state feedback and the inverted nonlinear system dynamics, NDI can significantly reduce controller development costs by avoiding gain scheduling and Jacobian linearization at a multitude of operating points. However, the control performance of NDI is directly dependent on required detailed knowledge of the model. As a simplified and enhanced NDI method , incremental nonlinear dynamic inversion (INDI) [4,5] has been proposed to reduce the model dependency and improve the robustness against model uncertainties. Instead of using a global nonlinear model, in INDI the dynamic inversion is implemented on a locally linearized system model that is updated at every sampling period, for which the control input is calculated in an incremental manner. Unlike NDI, for which full knowledge of the complete system dynamics is needed, INDI only requires explicit knowledge of the system’s control effectiveness matrix.