Paper
26 July 2004 Modeling and genetic-algorithm-based piezoelectric actuator configuration optimization of an adaptive circular composite plate
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Abstract
This paper presents an advanced strategy to determine the optimal configuration of piezoelectric actuators embedded in an adaptive circular composite plate (ACCP) with one central support and three simply supports on the edges for active vibration suppression. The modeling strategy combines the Finite Element Analysis (FEA) and the transfer function estimation technique taking into account the effects of piezoelectric patches on the dynamics of the structures. To obtain the optimal actuator locations coupled with the control law, the linear quadric regulator (LQR) is chosen as a controller to achieve maximum structural vibration suppression with minimum control energy consumption, where the norm-2 of the LQR optimal feedback gain vector is set as the objective function of the optimization strategy. Due to their effectiveness in searching optimal design parameters and obtaining globally optimal solution, the Genetic Algorithms (GAs) are applied to fmd the optimal actuator configuration and placement among 8 possible configurations. Finally, several simulations are performed using the LQR with five typical actuators configurations including the optimal configuration given by the GAs. The results show that a substantial saving in the objective function as well as a significant vibration reduction can be obtained when the optimal configuration of the actuators is adopted.
© (2004) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Su Yan and Mehrdad N. Ghasemi-Nejhad "Modeling and genetic-algorithm-based piezoelectric actuator configuration optimization of an adaptive circular composite plate", Proc. SPIE 5383, Smart Structures and Materials 2004: Modeling, Signal Processing, and Control, (26 July 2004); https://doi.org/10.1117/12.539383
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Cited by 6 scholarly publications.
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KEYWORDS
Actuators

Composites

Finite element methods

Optimization (mathematics)

Sensors

Genetic algorithms

Ferroelectric materials

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