Developing the Model Reduction Framework in High Frame Rate Visual Tracking Environment

Abstract

The developments in the field of aerospace materials and structures allow the more light weight air vehicles. However, the aircraft body, particularly wing, can deform more appreciably due to the occurrence of flow separation and flutter. Therefore, active control is necessary in order to maintain structural integrity. One of the proposed control methods uses visual tracking for structural state estimation, which reduces complexity in terms of hardware and data processing requirements compared to the conventional method using inertial measurement units and gyroscopes. However, the wing displacement measurement involves a high number of states to estimate. An idea is to implement a model reduction method to be implemented as a mathematical model of the aeroelastic wing to quicken the state estimation process. The proposed method of model reduction by using Modified Frequency-Limited Model Reduction (MFLMR) method by Gugercin and Antoulas (2004) is then augmented with the application of singular perturbation step and validated with simulation in stochastic Gaussian gust regimes for two wing models. The effect of additional singular perturbation step is presented. The results show that the proposed MFLMR method with singular perturbation prevails to replicate the true values in the simulated condition with different wing models in both time domain and frequency domain with smaller error autocorrelation. Further analysis is recommended to be focused on the implementation of the proposed model reduction method to the state and parameter estimation in order to maintain the high sample rate that can be attained by using the controller scheme with visual tracking.