Simulation and systems identification of helicopter dynamics using support vector regression

Abstract

This paper provides an overview of techniques developed for the application of Support Vector Regression (SVR) in the domain of simulation and system identification of helicopter dynamics. A generic high fidelity FLIGHTLAB helicopter model is used to train and validate a number of pitch response SVR models. These models are then trained using flight data from a Sikorsky Seahawk helicopter. The SVR simulation results show significant promise in the ability to represent aspects of a helicopter’s dynamics at a high fidelity. To achieve this, it is important to provide the SVR kernel with knowledge of past inputs that encompass the delay characteristics of the helicopter dynamic system. In this case, the use of Nonlinear Auto Regressive eXogenous input (NARX) network architecture achieves this goal. Good performance was achieved using input data that encompassed between 300 to 500 ms worth of historic response.