Indirect Intelligent Sliding Mode Control Using Hysteretic Recurrent Neural Networks With Application to a Shape Memory Alloy Actuated Beam

This paper presents the development of an indirect intelligent sliding mode controller (IISMC) for shape memory alloy (SMA) actuators. The controller manipulates applied voltage, enabling temperature control in one or more SMA tendons, which are offset to produce bending in a flexible beam tip. Hysteresis compensation is achieved using a hysteretic recurrent neural network (HRNN), which maps the nonlinear, hysteretic relationships between SMA temperatures and bending angle. Incorporating this HRNN into a variable structure control architecture provides robustness to model uncertainties and parameter variations. Single input, single output and multivariable implementations of this control strategy are presented. Controller performance is evaluated using a flexible beam deflected by single and antagonistic SMA tendons. Experimental results demonstrate precise tracking of a variety of reference trajectories for both configurations, with superior performance compared to an optimized PI controller for each system. Additionally, the IISMC demonstrates robustness to parameter variations and disturbances.