Modeling of shape memory alloy springs using a recurrent neural network

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.