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Sensing and control of a quadrotor using a visual inertial fusion method
Ping Li,Matthew Garratt,Andrew Lambert 제어로봇시스템학회 2015 제어로봇시스템학회 국제학술대회 논문집 Vol.2015 No.10
A visual inertial fusion method is proposed in this paper for the state estimation and control of a lowcost Unmanned Aerial Vehicle. A binary template matching algorithm is combined with a gradient based algorithm to compute optic flow (OF). The proposed OF method is capable of handling large displacement, illumination variation and gives subpixel accuracy. With a ground plane assumption, the Jacobian motion model is employed to solve for the unscaled linear velocity, which is fused with inertial measurements in an Extended Kalman Filter (EKF) framework to estimate metric speed and altitude. A number of flight tests have been conducted both indoors and outdoors to evaluate the performance of the proposed approach.
A Computationally Efficient Approach for NN Based System Identification of a Rotary Wing UAV
Mahendra Kumar Samal,Sreenatha Anavatti,Tapabrata Ray,Matthew Garratt 제어·로봇·시스템학회 2010 International Journal of Control, Automation, and Vol.8 No.4
Neural Network (NN) models based on autoregressive structures have long been used for nonlinear system identification problems. Their application for on-line implementations, however require them to be trained within a prescribed time span, which is often related to the sampling time of the system. In this paper, we introduce a NN model that is embedded with a dimensionality reduction mechanism in order to reduce the size of the network. The dimensionality reduction is based on Principal Component Analysis (PCA) and the resulting smaller NN trains faster. The longitudinal and lateral dynamics of a rotary wing Unmanned Aerial Vehicle (UAV) is modelled using flight test data. The re-sults of system identification, error statistics and training times are provided to highlight the benefits of the proposed approach for NN based system identification models.