Adaptive neural network controller for the rotating SMA actuator

Abstract

In this research, we present a new data-driven Adaptive Neural Network (ANN) controller designed for a unique arc-shaped Shape Memory Alloy (SMA) actuator. The actuator generates rotational motion through two-dimensional shape restoration, which is significantly different from traditional linear SMA actuators. The SMA in an arc-shaped pattern tries to recover its original shape when heated, ultimately leading to rotational motion. Due to complexity in SMA motion, it is difficult to apply conventional empirical modeling methods in this research. Given this modelling difficulty, a model-based control approach may not be promising. Thus, a data-driven ANN is adopted here to control the SMA actuator without relying on precise modelling. This controller adeptly learns the actuator's dynamic behavior in real-time, fine-tuning its neural network weights to ensure optimal control. To validate its efficacy, we compare performance of the ANN controller with that of the traditional Proportional-Integral-Derivative (PID) and sliding mode controllers across two reference inputs (sinusoidal and square) under four different disturbance scenarios (input-output, output-only, input-only, and no disturbance). Our experimental results show the ANN controller can provide similar or slightly better performance in terms of tracking accuracy and disturbance rejection without rigorous parameter or gain tuning compared to traditional controllers. Our results verify advanced learning capabilities of the ANN controller and its potential in control of the SMA actuator.