Tuning of a Complementary Orientation Filter Using Velocity Data and a Genetic Algorithm
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Authors
Maton, Dariusz
Economou, John
Khan, Irfan
Galvao Wall, David
Cooper, Rob
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Maton, Dariusz; Economou, John; Khan, Irfan; Galvao Wall, David; Cooper, Rob (2023). Tuning of a Complementary Orientation Filter Using Velocity Data and a Genetic Algorithm. Cranfield Online Research Data (CORD). Software. https://doi.org/10.17862/cranfield.rd.24807579
Abstract
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.
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Software Description
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Github
Keywords
'Orientation filter', 'Optimisation', 'IMU', 'Inertial data', 'Genetic Algorithm (GA)', 'MATLAB'
DOI
10.17862/cranfield.rd.24807579
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CC BY 4.0
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Industrial CASE Account - Cranfield University 2018