Taking full advantage of the favorable flight properties of biologically inspired flapping-wing micro aerial vehicles requires having insight into their dynamics and providing adequate control in all flight conditions. Because of the high complexity of flapping flight and limited availability of accurate flight data, however, global models are not readily available, particularly models validated with flight data and suitable for practical applications. This paper proposes an approach for global modeling of nonlinear flapping-wing dynamics, constructing a linear parameter-varying model from a set of local linear models. The model parameters and scheduling functions are determined using system identification, from free-flight data collected on a real test platform over a significant part of the flight envelope. The resulting model allows for the dominant parts of the dynamics to be accurately represented across the considered range of conditions. With 25 parameters overall, it significantly improves on the starting point of 46 local models with 12 parameters each. Moreover, a single model that adapts to the flight condition provides flexibility and continuous coverage, highly useful for simulation and control applications. Although in the explored part of the flight envelope nonlinearity was found to be limited, the approach is scalable and expected to also cover larger variations.
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