Vibration control of beams with piezoelectric sensors and actuators using particle swarm optimization |
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| Authors: | Magdalene Marinaki Yannis Marinakis Georgios E Stavroulakis |
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| Affiliation: | 1. Mus Alparslan University, Mus, Turkey;2. Department of Mathematical Engineering, YTU, Istanbul, Turkey;1. State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China;2. State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian 116024, China;3. National Laboratory for Scientific Computing, LNCC/MCTIC, Av. Getúlio Vargas 333, Quitandinha Petrópolis, Rio de Janeiro 25651-075, Brazil;4. Department of Mathematics and Statistics, Federal University of Pelotas, Campus Universitário, 354, Pelotas, Rio Grande do Sul 96010-900, Brazil;1. USN 5VX17PES93, VTU RRC, Belagavi, Karnataka, India;2. Department of Electronics & Communication Engg, JSS Academy of Technical Education (JSSATE), Noida, Uttar Pradesh, India;3. Department of Electronics & Communication Engg, Dayananda Sagar College of Engineering, Shavigemalleshwara Hills, Kumaraswamy Layout, Bangalore 560078, Karnataka, India;1. School of Mechanical Engineerin, REVA University, Bengaluru 560064, India;2. Department of Mechanical Engineerin, Vijay Vittala College of Engineering, Bengaluru 560064, India |
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| Abstract: | This paper presents the design of a vibration control mechanism for a beam with bonded piezoelectric sensors and actuators. The mechanical modeling of the structure and the subsequent finite element approximation are based on the classical equations of motion, as they are derived from Hamilton’s principle, in connection with simplified modeling of the piezoelectric sensors and actuators. One nature-inspired intelligence method, the Particle Swarm Optimization, is used for the vibration control of the beam. Three different variants of the Particle Swarm Optimization were tested, namely, the simple Particle Swarm Optimization, the inertia Particle Swarm Optimization and the Constriction Particle Swarm Optimization. A linear feedback control law and a quadratic cost function are used, so that the results are comparable with the classical linear quadratic regulator approach. The same problem has been solved with two other stochastic based optimization algorithms, namely a Genetic Algorithm and a Differential Evolution and the results are used for comparison. The numerical simulation shows that sufficient vibration suppression can be achieved by means of this method. |
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