{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/44926"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/44926","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Neural network identification of quarter-car passive and active suspension systems","abstract":"Much research effort has been done on the design of active suspension systems based on a quarter-car model with both full-state feedback and incomplete state feedback controllers to optimize the passenger ride comfort, road handling and car controlling. Linear stochastic optimal control will be employed to design an active controller in vehicle active suspension model. The active suspension model will be simulated and compared with the car's passive suspension model. Backpropagation neural networks then will be used to identify the passive suspension model with full state or incomplete state measurements. The accurate identification of the suspension system employing neural networks is used to reduce the number of sensors needed over full-state measurements.","abstract_html":"Much research effort has been done on the design of active suspension systems based on a quarter-car model with both full-state feedback and incomplete state feedback controllers to optimize the passenger ride comfort, road handling and car controlling. Linear stochastic optimal control will be employed to design an active controller in vehicle active suspension model. The active suspension model will be simulated and compared with the car's passive suspension model. Backpropagation neural networks then will be used to identify the passive suspension model with full state or incomplete state measurements. 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Linear stochastic optimal control will be employed to design an active controller in vehicle active suspension model. The active suspension model will be simulated and compared with the car's passive suspension model. Backpropagation neural networks then will be used to identify the passive suspension model with full state or incomplete state measurements. 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Linear stochastic optimal control will be employed to design an active controller in vehicle active suspension model. The active suspension model will be simulated and compared with the car's passive suspension model. Backpropagation neural networks then will be used to identify the passive suspension model with full state or incomplete state measurements. 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