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

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dc.contributor.authorMaton, Dariusz
dc.contributor.authorEconomou, John
dc.contributor.authorKhan, Irfan
dc.contributor.authorGalvao Wall, David
dc.contributor.authorCooper, Rob
dc.date.accessioned2024-06-09T15:02:17Z
dc.date.available2024-06-09T15:02:17Z
dc.date.issued2024-01-08 14:59
dc.description.abstractThe 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.
dc.description.sponsorshipIndustrial CASE Account - Cranfield University 2018
dc.identifier.citationMaton, 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
dc.identifier.doi10.17862/cranfield.rd.24807579
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/22169
dc.publisherCranfield University
dc.rightsCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject'Orientation filter'
dc.subject'Optimisation'
dc.subject'IMU'
dc.subject'Inertial data'
dc.subject'Genetic Algorithm (GA)'
dc.subject'MATLAB'
dc.titleTuning of a Complementary Orientation Filter Using Velocity Data and a Genetic Algorithm
dc.typeSoftware

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