Identification of Helicopter Dynamics based on Flight Data using Nature Inspired Techniques

1. Introduction

Helicopter system identification is the extraction of system characteristics/dynamics from measured flight test data (Maine & Iliff, 1985; Anon, 1991). The complexity of helicopter flight dynamics makes, modelling and helicopter system identification a very difficult task. Unlike fixed-wing aircrafts, the helicopters exhibits a high degree of inter-axis coupling, highly unstable, non-minimum phase dynamic characteristics and large response variations with flight condition. These characteristics of the helicopter make it a highly non-linear and a complex dynamical system. Further, wind tunnel/flight tests are required for the prediction of the aeromechanical forces - loads on rotor system and main rotor wake interferences with empennage/tail-rotor. But the wind tunnel experimental data suffers from scale effects and model deficiencies. Therefore, a key tool for helicopter flight/ground test correlation is provided by system identification using flight data.

Identification of a system requires picking a function (or model) to approximate the input-output behaviour of the system (the helicopter in this case) in the “best” possible manner. There has been considerable amount of work carried out in this regard, exploring the various methods available for identification of dynamical systems (Miller, Sutton, & Werbos, 1990; Narendra & Parthasarathy, 1989; Narendra & Parthasarathy, 1990; Narendra & Parthasarathy, 1991; Ichikawa & Sawa, 1992; Sastry, Santharam, & Unnikrishnan, 1994; Chen, Billings, & Grant, 1990; Hoskins, Hwang, & Vagners, 1992). Identification of nonlinear physical models continues to be a challenge since both the structure and parameters of the physical model must be determined. Many existing system identification methods are based on parametric identification. Structure determination often uses a trial and error approach to test candidate model structures. Possible structures are deduced from engineering knowledge of the system and the parameters of these models are estimated. But in the case of a helicopter, defining an a priori model is difficult due to interaction between the various subsystems like the rotor, fuselage, power plant, tail rotor and transmission systems (Tischler, 1996) the dynamics are of relatively higher order, and it is difficult to know how many states to include and which states are important. Also, increase in the nonlinearity, uncertainty and complexities of the model together with the stringent specifications of accuracy limits to be maintained renders modelling helicopter systems a daunting task. This initiated an interest among researchers to identify the system characteristics using nonparametric methods.