Structural Health Monitoring for Large Smart Structures via Optimal Control Design
- Paper number
IAC-05-C2.4.04
- Author
Dr. Paul Williams, Australia
- Coauthor
Prof. Pavel M. Trivailo, RMIT University, Australia, Australia
- Year
2005
- Abstract
This paper presents a methodology for designing a control system that enhances the ability to detect and quantify damage in large smart space structures. The methodolody is a multi-step procedure that involves determining the positions of actuators and designing a control system that enhances the sensitivity of the closed-loop system to changes in the system parameters. The optimal location of control actuators is determined using a specialized genetic algorithm which is designed to maximize a measure of modal controllability for second order systems. The performance index represents a measure of output controllability that measures both modal controllability and the modal participation of all modes in a separately chosen (physically important) cost function. For the optimal location of the control actuators, an additional control system is designed that specifically addresses the problem of damage detection. The control system utilizes full state feedback combined with acceleration feedback. The acceleration feedback effectively modifies the closed-loop mass matrix, which is used to enhance the closed-loop frequency sensitivity to damage. The control system is designed using linear quadratic regulator theory to minimize stresses in the system components as well as the control energy. The acceleration feedback gains are determined optimally using simulated annealing so as to maximize the sensitivity of the closed-loop frequencies to damage. A damage detection algorithm is then proposed using nonlinear programming to solve the inverse problem of finding the system parameters to give the closed-loop frequencies. In our study, we account for noise in the measured frequencies and study a range of different scenarios.
- Abstract document
- Manuscript document
IAC-05-C2.4.04.pdf (🔒 authorized access only).
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