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- W2945651305 abstract "Active vibration suppression is a well explored area when it comes to simple problems, however as the problem complexity grows to a time variant system, the amount of researched solutions drops by a large margin, which is further increased with the added requirement of very limited knowledge about the controlled system. These conditions make the problem significantly more complicated, often rendering classic approaches suboptimal or unusable, requiring a more intelligent approach - such as utilizing soft computing. This work proposes a Artificial Neural Network (ANN) Model Predictive Control (MPC) scheme, inspired by horizon techniques which are used for MPC. The proposed approach aims to solve the problem of active vibration control of an unknown and largely unobservable time variant system, while attempting to keep the controller fast by introducing several methods of reducing the amount of calculations inside the control loop - which with proper tuning have no negative impact on the controller’s performance. The proposed approach outperforms the multi-input Proportional-Derivative (PD) controller preoptimized using a genetic algorithm." @default.
- W2945651305 created "2019-05-29" @default.
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- W2945651305 date "2019-01-01" @default.
- W2945651305 modified "2023-10-05" @default.
- W2945651305 title "Neural Net Model Predictive Controller for Adaptive Active Vibration Suppression of an Unknown System" @default.
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- W2945651305 doi "https://doi.org/10.1007/978-3-030-20912-4_10" @default.
- W2945651305 hasPublicationYear "2019" @default.
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