Helicopter parameters estimation from subspace identification by constrained nonlinear optimization

Abstract

Subspace identification uses well-understood techniques based on linear algebra and numerical methods. However, the state space model matrices which are obtained from conventional subspace identification algorithms are not necessarily associated with the physical states. This may be evaluated as a deficiency for the area of helicopter flight dynamics where physical parameter estimation is mainly conducted for mathematical model improvement, aerodynamic parameter validation and flight controller tuning. There are a limited number of studies in literature, which tackle this problem. Some of these studies are based on nonlinear optimization. However this optimization problem may have infinitely many solutions if we do not define well-founded constraints. It may be possible to estimate the real physical parameters by establishing the constraints which compatible with practical values. This study focuses on to the determination physical constraints for the parameters which are confined to the problem described here. For this purpose, the subjected parameters are examined according to their physical meaning. Both the expected theoretical values and the experimental knowledge are evaluated to determine the constraints. Then, many runs are conducted for these predefined constraints with randomly selected initial conditions