Research of Time Delay in Active Structural Control

• Main Authors: Cho-Yen Yang, 楊卓諺
• Other Authors: An-Pei Wang
• Format: Others
• Language: zh-TW
• Published: 2005
• Online Access: http://ndltd.ncl.edu.tw/handle/5ge79t

碩士 === 中原大學 === 土木工程研究所 === 93 === The purpose of this research is to study about time delay effect in active structural control. If time delay is occur in control system, then we cannot ensure not only the control system is stable but also the structure response convergence. We attempt to establish a neural network predictor in order to predict the state of structure, and modify the controller to compensate the time delay effect. There are two time delay compensations being used usually. One is phase shift compensation that should be identified the mode of structure and delay time. Another is solving the equation of motion on-line. This method is more efficient despite of huge number of calculations. Two kinds of controller were selected as follows: one is variable structure control system (VSS). Another is fuzzy-neural network (FNN). And two time delay compensation ways of phase shift and time delay neural network. Thus, we have two kinds of controller and two kinds of compensation ways and four combinations. Each of them are selected and compared with others. The results of numerical simulation in single degree of freedom (SDOF) structure: if the controller is FNN with delay time less than three steps, both of phase shift and time delay neural network predictor could compensate the time delay effect efficiently. On the other hand, if the controller changed to VSS with delay less than fifteen steps, both of two compensation ways could make the response become lower. However, the time delay neural network predictor is better than phase shift compensation in response of displacement, velocity and control force. In the case of three degree of freedom structure: system delay one step may cause the response of structure diverge. Phase shift compensation and time delay neural network predictor can decrease the displacement and velocity response in system delay one step. Keywords: time delay, phase shift, TDNN, neural network