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Research On Active Control Of Seismic Response For Long Span Cable-Stayed Bridge

Posted on:2009-07-16Degree:DoctorType:Dissertation
Country:ChinaCandidate:X F LiFull Text:PDF
GTID:1102360272470993Subject:Structural engineering
Abstract/Summary:PDF Full Text Request
As the important infrastructure, bridge is an important component of the seismic disaster prevention system and crisis management system. Improving the bridge seismic performance is one of the basic measures to reduce earthquake damage and strengthen regional security. Building structure vibration controlling has been proven to be an effective means to withstand earthquake damage by the theory and practice after decades of development. With long-span bridges constructed and extensive usage of high-performance material, bridge structure vibration problem had become more prominent. But because of the special nature of the long-span cable-stayed bridge, the level of bridge vibration control is far from the high-building vibration control. Therefore, base of understanding the bridge seismic response characteristics, studying the reasonable and practicable control measures to protect the bridge structure against earthquake damage will be a great project.With the development of science and the ever-increasing demands for vibration environment and structure vibration characteristics, passive vibration control was difficult to satisfy and exposed its limitation. Active control became a new important ways because of its effective and adaptable. This paper will study bridge structure active control and its key problems against earthquake base of long-span cable-stayed bridge. So improve the bridge anti-seismic capability and enhance its dynamical stability.Firstly, further improve benchmark performance system of cable-stayed bridge active control base of original indicators. More comprehensively evaluates different active control strategy for cable-stayed bridge. Then study the theory of balance reduction and its achieving. Reduce the model by internal balance transform for quadratic characteristic indexes function and find out the state variables which are great contribution to system energy. So obtain a reduced model which can reflect the dynamic response of original model in order to meet the active control of large-scale civil engineering.In the design of active control system, research the optimal number and position of actuators/sensors. Determine the optimal number of actuators according to the state effect matrix by controlling. According to the quadratic performance index and the proposed optimal sensors distribution guidelines, design optimal actuators/sensors configuration and optimize the calculation process. So obtain the optimal distribution of actuators/sensors for active vibration control of cable-stayed bridge.Iterative learning control is a more satisfactory control strategy, with its own intelligence , which be able to constantly improve itself in controlling process. So it is gradually becoming an issue of concern. Based of the latest research results in the control field, combine the iterative learning control and other control strategy to reduce the earthquake response of cable-stayed bridge. Firstly, combine the linear quadratic optimal control and iterative learning control to obtain a new mixed control strategy which named quadratic iterative learning control. It can improve the convergence rate of iterative learning control and improve the effect of control. Secondly, with respective advantages of iterative learning control and sliding mode control strategy, combine them and obtain a new control strategy which named slide mode iterative learning control. Use these two new mixed control strategies to control the Emerson Memorial Bridge against earthquake and calculate the benchmark performance indicators. The result show that new control strategies were able to effectively control the Emerson Memorial Bridge against earthquake and control effect were improved.
Keywords/Search Tags:Long-span cable-stayed bridge, Active control, Benchmark performance indicators, Internal balance system, Model reduction, Actuator/sensor, Iterative learning control
PDF Full Text Request
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