A Neural Network Method for Miniature Unmanned Helicopter Heading Control

A neural network control method for heading control of miniature unmanned helicopter is proposed. For the complexity of miniature helicopter aerodynamics, it is difficult to identify the unknown parameters of yaw dynamics model. To design heading controller of miniature helicopter without modelling yaw dynamics, BP neural network is designed as heading controller, which is trained by collected flight data. By training, the neural network controller can learn the artificial operation strategy and realize the heading control of miniature unmanned helicopter. Simulation results demonstrate the validity of the proposed neural network control method.

Helicopter icing

Due to constraints of natural condition, cost and of available time associated with model fabrication and for extensive wind-tunnel tests or flight tests, Computational Fluid Dynamics (CFD) simulation was considered an alternative means of providing air vehicle icing simulation and aeromechanic performance analysis. Full-scale icing experiments and, therefore, certification and cost can be significantly reduced by developing full-numerical simulation methods to evaluate the air vehicle performance for a wide range of icing conditions. This paper summarises helicopter icing simulation methods that include the development of helicopter aerodynamics, calculation methods of helicopter icing, icing protection system performance, icing effects on the helicopter performance, and some challenges in helicopter icing simulation.