Micropositioning using shape memory alloy actuators

Shape memory alloys (SMA) can be used to generate motion or force in electromechanical devices and micro-machines, although their accuracy is severely limited by their highly nonlinear and hysteretical stimulus-response characteristics. In this work we present some results regarding a nonlinear control method suitable for SMA-based positioning applications. In particular, we show how the hysteresis effects can be compensated using an inverse hysteresis model generated by a neural network, trained using experimental data. The control strategy, experimented on a laboratory SMA actuator, uses the inverse model inserted in a proportional-integral with antiwindup control loop. It is found that neural networks successfully improve the closed-loop response, leading to position accuracies close to a micrometer.