Due to ill-defined and poorly understood dynamics of flapping wing flying robots, acquiring high-fidelity aero dynamical and control models for such aerial robots are challenging and often infeasible. In this work, we propose using a non-linear, model-free approach that eliminates errors arising from the mis-modeling or under-modeling of flapping dynamics. These procedures require no prior knowledge or model of any of the dynamical aspects of the flying robot and assume zero statistical knowledge about noise affecting sensor measurements. Intelligent PID (iPID) controllers are used to provide an abstracted treatment of all unknowns and uncertainties in the robot by encapsulating them together into a single term. The problem then devolves into the estimation of this term by either direct or indirect means which is more practical than obtaining high-fidelity models. For indirect estimation, numerical differentiation is required and we propose using a Haar-wavelet differentiator that is capable of smoothing and differentiating signals irrespective of the multivariate noise corrupting the signal. The direct estimation method uses an algebraic and non-asymptotic method. Pitch control and altitude control experiments are successfully performed on a real, bird-like flapping wing flying robot using all different approaches; results obtained reveal that indirect estimation using the Haar-wavelet differentiator provides slightly better performance.
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