Modeling for Rotational Speed of Helicopter Rotor Based on Wavelet Support Vector Machine

Neural networks with good nonlinear mapping abilities can be applied to build simulation model of helicopter. But they have some difficulties such as hardness of selecting network structure, slow convergence speed, local minimum, and over-fitting. To avoid above problems, a method for building simulation model of helicopter based on Wavelet Support Vector Machine (WSVM) is proposed. Marr wavelet is used to construct one-dimension wavelet kernel, and the rationality of the wavelet kernel is proved. Subsequently the wavelet kernel is extended to multi-dimensional case. Based on pretreatment of practical flight data, rotational speed model for landing process of helicopter with rotor self-rotating is built with WSVM. Compared with neural network model, WSVM model possess some advantages such as simple structure, fast convergence speed and high generalization ability. It is shown by theoretic analysis and simulation results that WSVM method to build simulation model of helicopter is feasible.