Controlling the Deformation of the Antagonistic Shape Memory Alloy System by LSTM Deep Learning

The antagonistic system of two shape memory alloy wires is a great inspiration for the robotics field where it is applied as a linear actuator due to its shape memory effect. However, its control is still a challenge due to its hysteresis behavior. For that reason, a new controller is proposed in this paper for the displacement of the system’s effector. It is based on a Long Short-Term Memory neural network model. The aim is achieved by combining temperature-deformation data from an analytical model with voltage-temperature-deformation data from real experiments. Hence, these datasets are studied to overcome the nonlinearity obstacle of this system in order to be able to integrate it into robotic applications.