Modeling of Fluid Powered Actuators Using Auto Regressive with Exogenous Input Model

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dc.contributor.authorHossain, Zakia
dc.contributor.examiningcommitteeWang,Quan (Abraham) (Mechanical & Manufacturing Engineering) Fung,Wai-Keung (Electrical and Computer Engineering)en_US
dc.contributor.supervisorSepehri,Nariman (Mechanical & Manufacturing Engineering)en_US
dc.date.accessioned2012-09-26T00:50:43Z
dc.date.available2012-09-26T00:50:43Z
dc.date.issued2012-09-25
dc.degree.disciplineMechanical and Manufacturing Engineeringen_US
dc.degree.levelMaster of Science (M.Sc.)en_US
dc.description.abstractSystem identification has importance in modeling and control of industrial systems. The main task of system identification is to build a suitable model that represents the relationship between input, output and disturbances of a real system. The thesis presents identification and discrete time linear modeling of a hydraulic actuator. This thesis demonstrates how to formulate hydraulic functions for both normal and faulty conditions with internal leakage using both offline and on-line measurements. Least square and recursive least square methods are used to estimate the model parameters based on the Auto Regressive technique with Exogenous input (ARX) model. For the offline case, square and sine wave signals are used as input control signals. For the online case, random input control signal is applied. Prediction error criterion is used for model validation based on experimental data. It is shown that the ARX model is capable of representing a valve-controlled hydraulic system dynamics.en_US
dc.description.noteOctober 2012en_US
dc.identifier.urihttp://hdl.handle.net/1993/9151
dc.language.isoengen_US
dc.rightsopen accessen_US
dc.subjectSystem identificationen_US
dc.subjectmodelingen_US
dc.subjectARXen_US
dc.subjectHydraulic actuatoren_US
dc.subjectInternal leakageen_US
dc.subjectonlineen_US
dc.subjectofflineen_US
dc.titleModeling of Fluid Powered Actuators Using Auto Regressive with Exogenous Input Modelen_US
dc.typemaster thesisen_US
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