Neuro-fuzzy model based feedback controller for Shape Memory Alloy actuators

This paper describes development of a motion controller for Shape Memory Alloy (SMA) actuators using a dynamic model generated by a neuro-fuzzy inference system. This kind of smart alloy is known to have a unique characteristic in that its shape can be controlled by temperature that can be varied by passing a current through it, rising SMA actuators, it would be possible to design miniature mechanisms for a variety of applications. Today SMA is used for valves, latches, and locks, which are automatically activated by heat. However it has not been used as a motion control device due to difficulty in the treatment of its highly non-linear strain-stress hysteresis characteristic which is further influenced by its temperature. In this project, a dynamic model of a SMA actuator is developed using ANFIS, a neuro-fuzzy inference system provided in MATLAB environment. Using neuro-fuzzy logic, the system identification of the dynamic system is performed by observing the change of state variables (displacement and velocity) responding to a known input (voltage across the SMA actuator). Then, using the dynamic model, the estimated input voltage required to follow a desired trajectory is calculated in an open-loop manner. The actual input voltage supplied to the SMA actuator is the sum of this open-loop input voltage and an input voltage calculated from an ordinary PD control scheme. This neuro-fuzzy logic-based control scheme is a very generalized scheme that can be used for a variety of SMA actuators. Experimental results are provided to demonstrate the potential for this type of controller to control the motion of the SMA actuator.