Hovering control based on fuzzy neural networks for biomimetic flying robotic

Bio-Inspired Flapping Wing Micro Air Vehicles (BIFW MAVs) are highly nonlinear and overactuated system. Besides, they may suffer from various uncertainties and perturbations like wind gust, sensor error etc. Modelling of such complex nonlinear system by considering the uncertainties is very difficult for the conventional first principle methods. However, numerous advantages of BIFW MAVs such as vertical take-off and landing, hovering, quick turn, and enhanced manoeuvrability attract researchers to develop their accurate modelling, and to do so Evolving Intelligent Systems (EISs) is an appropriate candidate since they do not need any information about the system dynamics. In this work, an advanced EIS called Generic Evolving Neuro-Fuzzy Inference System (GENEFIS) is employed to identify a four-wing BIFW MAV Multi Input Multi Output nonlinear model on the fly from the data stream, where an efficient online identification of the BIFW MAV model is observed.

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Hovering control based on fuzzy neural networks for biomimetic flying robotic

Cited by 2 publications

References 7 publications

Dynamics, stability, and control analyses of flapping wing micro-air vehicles

Dynamics, stability, and control analyses of flapping wing micro-air vehicles

Evolving neuro-fuzzy system based online identification of a bio-inspired flapping wing micro aerial vehicle

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