Improved Neural Adaptive Control for Nonlinear Oscillatory Dynamic of Flapping Wings

Abstract

Modeling uncertainties and oscillatory dynamics are control challenges for flapping vehicles. Flapping-wing aerial vehicles are nonlinear time-varying oscillatory systems with flexible multibody dynamics. As such, accurate modeling of these complex systems considering unsteady aerodynamics is a formidable task. Though adaptive controllers can work well with uncertain approximate models, inherent oscillation of flapping systems can degrade their control performance and create undesired control actuations. This paper addresses improvements of neural adaptive dynamic inversion controller toward efficient performance for flapping flight via effective utility of actuator capacity. In this respect, first an assumptive model of a bird-mimetic flapping-wing is considered for simulation and analysis. It is shown that the usage of oscillatory feedback data produces additional error in trajectory tracking and unnecessary oscillatory control actuations. Subsequently, the control loop is modified by adding an adaptive notch filter for real-time estimation, tracking, and removal of dominant oscillatory modes from the feedback data and consequently from the controller output. As a result, the required control effort is effectively reduced and the controller performance is significantly improved. Simulations are provided to demonstrate the efficiency of the proposed scheme in presence of noise, variation of system frequency, disturbances, as well as delay in the control loop.