Extended Kalman filter and Takagi-Sugeno fuzzy observer for a strip winding system

Szedlak-Stînean, Alexandra-Iulia; Precup, Radu; Petriu, Emil M.; Roman, Raul‐Cristian; Hedrea, Elena‐Lorena; Bojan-Dragoş, Claudia-Adina

doi:10.1016/j.eswa.2022.118215

Cited by 26 publications

(1 citation statement)

References 70 publications

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“…Predictor-corrector filters and observers are widely used for state and parameter estimation [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. These filters can be roughly divided into two categories: those aiming for optimal solutions and those focused on maintaining stability [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

The application of sliding innovation filter as estimation strategy applied to a UAV

AlShabi,

Gadsden,

Khuwaileh

et al. 2024

Unmanned Systems Technology XXVI

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The unmanned aerial vehicle (UAV) and unmanned aerial system (UAS) are popular in nowadays applications including military, industry, weather casting, monitoring, and many other applications. According to several research, the system must be controlled in precise way to make sure that the UAV and UAS are moving in the desired trajectories. However, this task is not an easy task in real life due to the presence of disturbances and noise in feedback measurements. To overcome this issue, researchers either developed more stable controllers, i.e. active disturbance rejection control (ADRC), or they improved the measured signals using filters with more accurate/stable performance. This work belongs to the second category, where a newly developed filter, which is referred to as sliding innovation filter (SIF), is used to estimate the states of a UAV system while it is tracking a target at the same height to improve the quality of the controller.

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