English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Control performance of Unmanned Aerial Vehicles (UAVs) is directly affectedby their ability to estimate their states accurately. With the increasingpopularity of autonomous UAV solutions in real world applications, it isimperative to develop robust adaptive estimators that can ameliorate sensornoises in low-cost UAVs. Utilizing the knowledge of UAV dynamics in estimationcan provide significant advantages, but remains challenging due to the complexand expensive pre-flight experiments required to obtain UAV dynamic parameters.In this paper, we propose two decoupled dynamic model based Extended KalmanFilters for UAVs, that provide high rate estimates for position, and velocityof rotational and translational states, as well as filtered inertialacceleration. The dynamic model parameters are estimated online using the DeepNeural Network and Modified Relay Feedback Test (DNN-MRFT) framework, withoutrequiring any prior knowledge of the UAV physical parameters. The designedfilters with real-time identified process model parameters are testedexperimentally and showed two advantages. Firstly, smooth and lag-freeestimates of the UAV rotational speed and inertial acceleration are obtained,and used to improve the closed loop system performance, reducing the controlleraction by over 6 %. Secondly, the proposed approach enabled the UAV to trackaggressive trajectories with low rate position measurements, a task usuallyinfeasible under those conditions. The experimental data shows that we achievedestimation performance matching other methods that requires full knowledge ofthe UAV parameters.

Dynamic Based Estimator for UAVs with Real-time Identification Using DNN and the Modified Relay Feedback Test