Fuzzy Gain Scheduling for Flutter Suppression in an Unmanned Aerial Vehicle

The active flutter suppression of the flexible modes of a generic model of an unmanned aerial vehicle that exhibits open-loop nonminimum phase behavior and three flutter mechanisms is investigated. A fuzzy gain scheduler interpolates plant models, state feedback, and observer gains based on an exogenous input velocity that is frozen in time. The novelty of the approach rests on the fuzzy interpolation of high-order models and on the method of gain table construction. Closed-loop stability over a wide range of velocities is demonstrated via time simulations for both linear time-invariant plant models and their fuzzy approximations. The fuzzy gain scheduling algorithm demonstrates stronger stability characteristics and generalization capabilities over pointwise synthesized linear quadratic Gaussian controllers, reduced-order controllers, and recurrent neural networks. The resultant global linear controller extends the flutter boundary to a velocity approximately 60% higher than the first open-loop flutter onset speed.

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