Force Control of SMA Springs Using RNNs

Shape memory alloys (SMAs), generally composed of nickel-titanium, have unique properties that enable them to transition from the martensite phase to the austenite phase when heated. During this transformation, SMAs can shorten and revert to their original shape, which makes them valuable as actuators. SMAs’ biocompatibility, high power-to-weight ratio, low noise, and ability to generate substantial force position them as essential materials in robotic and biomedical applications. However, their control is complicated by inherent nonlinearity and hysteresis. When cooled in the austenite phase, SMAs elongate and deform, with different phase transformation temperatures observed during heating and cooling due to hysteresis. These complexities highlight the need for advanced control techniques. Artificial intelligence models, particularly recurrent neural networks (RNNs), are well-suited for capturing the intricate relationships within SMA behavior. This paper introduces an approach that utilizes RNNs and real-time measurements for force control in SMAs. A compact test setup equipped with sensors collects data on voltage, current, and generated force, enabling the system’s dynamic behavior analysis. RNNs effectively model the nonlinear characteristics of SMA actuators, leveraging their capacity to learn complex temporal relationships from the collected data. This approach is shown to enhance the control of SMAs over conventional control schemes, i.e., Proportional Integral Derivative (PID) control, and expand their applications in various fields.