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Dynamics and energy harvesting control of an autoparametric pendulum-like system

Mitura A., Kecik K., Augustyniak M.

Przegląd Elektrotechniczny

2018

R. 94, nr 7

82--85

This paper describes experimental rig used to research system with a pendulum. The pendulum has a double functions. Firstly, it is applied as a non-linear vibration absorber for a simple oscillator. Secondly, an additional magnets-coil subsystem mounted inside a pendulum structure gives possibility of energy recovery. Influence of an oscillator suspension damping and stiffness control on effectiveness of both effects are presented.

Praca opisuje stanowisko eksperymentalne wykorzystywane do badań systemu z wahadłem. Wahadło ma podwójną funkcję. Po pierwsze, jest zastosowane jako nieliniowy eliminator drgań prostego oscylatora. Dodatkowy podukład magnesów i cewki zamontowany na wahadle umożliwia odzyskiwanie energii z jego ruchu. Przedstawiono wpływ sterowania tłumieniem lub sztywnością zawieszenia na skuteczność obu efektów.

Modeling of shape memory alloy springs using a recurrent neural network

Kardan I, Abiri R., Kabganian M., Vahabi M.

Journal of Theoretical and Applied Mechanics

2013

Vol. 51 nr 3

711--718

In this paper, a recurrent neural network structure is proposed for the modeling of the behavior of shape memory alloy springs. Numerous mathematical modeling and experimental evaluations show that the force exerted by SMAs, aside from their length and applied voltages, depends on the loading path. Therefore, in addition to the applied voltage and deformation, a feedback of the voltage applied to, and the force exerted by the SMA spring in the previous time step is included in the inputs to this neural network to represent the loading path. Fed by adequate inputs, the NN estimates the output force of the spring. The results of some thermal loadings of the spring at various fixed lengths and mechanical loadings at various constant voltages are used to train the NN. The performance of the NN model is then evaluated for some constant weight loadings which are not learnt by the NN. Simulation results indicate that compared to other neural network structures, the proposed structure learns the behavior of the SMA spring faster (in less iteration). Moreover, it provides a more general model, i.e. this NN model effectively estimates the output force for almost all possible loadings.