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GA-based orientation filter tuning.zip (920.96 kB)

Tuning of a Complementary Orientation Filter Using Velocity Data and a Genetic Algorithm

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Version 3 2024-01-08, 14:59
Version 2 2023-12-14, 16:33
Version 1 2023-12-14, 15:03
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posted on 2024-01-08, 14:59 authored by Dariusz MatonDariusz Maton, John EconomouJohn Economou, Irfan KhanIrfan Khan, David Galvao WallDavid Galvao Wall, Rob CooperRob Cooper

The data uploaded here contains the experimental and simulation data used to demonstrate the utility of the optimisation of a complementary orientation filter using a genetic algorithm (GA).

Implementation of the GA in MATLAB is provided as well as supporting functions such as the zero velocity update and weighted-relative velocity error metric (W-RVE).

The novelty of the work is the optimal tuning of the complementary filter gain using a GA and velocity data of an object moving in the locally level frame. Optimal filter gains are encoded a Takagi-Sugeno (TS) fuzzy inference system with four Gaussian membership functions. This offers a transparent and traceable encoding.

Funding

Industrial CASE Account - Cranfield University 2018

Engineering and Physical Sciences Research Council

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Authoriser (e.g. PI/supervisor)

d.maton@cranfield.ac.uk

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