Data Fusion Based KF-UI With Real Time Estimation Of Dynamic Displacement And Decentralized Control

Structural health monitoring and vibration control play an important role in structural safety and reliability assessment.Structural dynamic load is an important foundation for structural design and structural health monitoring.However,it is difficult to realize the direct measurement of the structural dynamic load.Therefore,it is of great importance to study the dynamic excitation by measuring the structural responses.In addition,the structural dynamic displacement is a very important physical parameter to describe the vibration characteristics of the structure.In order to conduct on-line monitoring and system identification of structural parameters,the availability of acceleration response measurements as well as displacement response data is often required.From measured displacement,not only velocity,acceleration and deflection can be obtained,but also physical parameters of a structure can be estimated.Therefore,the identification of structural dynamic displacement is also essential for structural health monitoring.The masterstroke of this paper is methodology of data fusion based Kalman filter with unknown inputs.It aimed at overcoming the limitation of that health monitoring method needs to know the structural external load.At the same time,the data fusion is used to deal with the drifts brought by measurement noise.What’s more,the proposed method is applied to the identification of structural dynamic displacement and wind load identification.Finally,the proposed method is extended to generalized extended Kalman filter with unknown inputs(GEKF-UI),and the GEKF-UI is combined with the decentralized control.In the first part of this thesis,the methodology of data fusion based Kalman filter with unknown inputs has been proposed in order to dealing with the disadvantages of the previously work.Numerical examples have proved that this method can realize the real-time identification of the structural state and excitation exactly even if only partial responses are observed.In addition,the drifts brought by measurement noise can be limited by data fusion.In the second part of this thesis,the methodology of multi-rate data fusion based dynamic displacement estimation has been put forward.The main idea of this method is that considering the unknown acceleration bias as unknown excitation,and then using the method that has been proposed in the first part.Some numerical examples have proved that this method can identify the structural dynamic displacement and identify the unknown acceleration deviations.In the third part of this thesis,the proposed method is applied to the identification of distributed wind load.Suppose the distributed wind load is composed by a distribution function represents the distribution parttern of the wind load and another function represents the time history.These two functions are independent of each other.The mode shape functions are selected as the orthogonal basis for distribution function.Then,combing with the modal Kalman filter with unknown inputs,the distributed wind load can be identificated.In the last part of this thesis,the proposed method has been extended to generalized extended Kalman filter with unknown inputs(GEKF-UI).This method can realize the real-time identification of the structural state,structural parameters and excitation exactly even if only partial responses are observed.Furthermore,The GEKF-UI and the decentralized control are combined to realize the integration of structural identification and vibration control.