Abstract
AbstractIn this paper, a nano-quadrotor (40 g, diameter less than 0.10 m) is used for the self-controlled perching task on vertical wall surfaces using an adaptive neural network model. Due to its very light weight, the effect of aerodynamics drag is more severe. The proposed neural control scheme is developed for unmodelled dynamics of the nano-quadrotor. The unmodelled dynamics are estimated using a Chebyshev neural network. This single-layer neural network is adopted for developing the control laws for perching on vertical structures. The Lyapunov theory-based analysis is utilized to derive the update law for weights of the neural network. The proposed control algorithm confirms robustness under unknown dynamics of nano-quadrotor. The validation of system is demonstrated by experiments which shows the effectiveness of proposed control approach in perching application of nano-quadrotor.KeywordsQuadrotorNeural network-based controlChebyshev neural networkFeedback linearization
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