Paper
27 March 2012 Linear and non-linear systems identification for adaptive control in mechanical applications vibration suppression
Gabriele Cazzulani, Ferruccio Resta, Francesco Ripamonti
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Abstract
During the last years, more and more mechanical applications saw the introduction of active control strategies. In particular, the need of improving the performances and/or the system health is very often associated to vibration suppression. This goal can be achieved considering both passive and active solutions. In this sense, many active control strategies have been developed, such as the Independent Modal Space Control (IMSC) or the resonant controllers (PPF, IRC, . . .). In all these cases, in order to tune and optimize the control strategy, the knowledge of the system dynamic behaviour is very important and it can be achieved both considering a numerical model of the system or through an experimental identification process. Anyway, dealing with non-linear or time-varying systems, a tool able to online identify the system parameters becomes a key-point for the control logic synthesis. The aim of the present work is the definition of a real-time technique, based on ARMAX models, that estimates the system parameters starting from the measurements of piezoelectric sensors. These parameters are returned to the control logic, that automatically adapts itself to the system dynamics. The problem is numerically investigated considering a carbon-fiber plate model forced through a piezoelectric patch.
© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Gabriele Cazzulani, Ferruccio Resta, and Francesco Ripamonti "Linear and non-linear systems identification for adaptive control in mechanical applications vibration suppression", Proc. SPIE 8341, Active and Passive Smart Structures and Integrated Systems 2012, 83411V (27 March 2012); https://doi.org/10.1117/12.915190
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Cited by 9 scholarly publications.
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KEYWORDS
Control systems

Adaptive control

Systems modeling

System identification

Detection and tracking algorithms

Matrices

Target acquisition

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