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The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material.Tue, 06 Aug 2024 05:37:49 GMT2024-08-06T05:37:49ZBAYESIAN CONTROL OF A HELICOPTER MAIN GEARBOX SEMI-ACTIVE SUSPENSION SYSTEM - EXPERIMENTS ON A QUARTER-SUSPENSION PROTOTYPE
http://hdl.handle.net/10985/17017
BAYESIAN CONTROL OF A HELICOPTER MAIN GEARBOX SEMI-ACTIVE SUSPENSION SYSTEM - EXPERIMENTS ON A QUARTER-SUSPENSION PROTOTYPE
RODRIGUEZ, Jonathan; MALBURET, François
This short paper considers the control of a helicopter gearbox semi-active suspension. As the future generation of helicopters will include variable engine RPM during flight, it is interesting to consider implementing control on their suspension systems in order to always optimally filter the main disturbance frequency. Here, a semi-active suspension based on the DAVI principle is developed, simulated and tested with its control algorithm based on Bayesian optimization. This control method based on the Bayes theorem is a trial/error algorithm allows to significantly reduce the number of evaluations of the real objective function for a given set of parameters. Thus the system is capable to fastly determine its own optimal set of parameters to maximize the objective function. The objective is to prove experimentally the ability of the Bayesian optimization to lead the learning behavior of a semi-active resonant suspension.
Sun, 01 Jan 2017 00:00:00 GMThttp://hdl.handle.net/10985/170172017-01-01T00:00:00ZRODRIGUEZ, JonathanMALBURET, FrançoisThis short paper considers the control of a helicopter gearbox semi-active suspension. As the future generation of helicopters will include variable engine RPM during flight, it is interesting to consider implementing control on their suspension systems in order to always optimally filter the main disturbance frequency. Here, a semi-active suspension based on the DAVI principle is developed, simulated and tested with its control algorithm based on Bayesian optimization. This control method based on the Bayes theorem is a trial/error algorithm allows to significantly reduce the number of evaluations of the real objective function for a given set of parameters. Thus the system is capable to fastly determine its own optimal set of parameters to maximize the objective function. The objective is to prove experimentally the ability of the Bayesian optimization to lead the learning behavior of a semi-active resonant suspension.