Sliding mode control of a shape memory alloy actuated active flexible needle

Abstract

SUMMARYIn medical interventional procedures such as brachytherapy, biopsy and radio-frequency ablation, precise tracking through the preplanned desired trajectory is very essential. This important requirement is critical due to two major reasons: anatomical obstacle avoidance and accurate targeting for avoiding undesired radioactive dose exposure or damage to neighboring tissue and critical organs. Therefore, a precise control of the needling device in the unstructured environment in the presence of external disturbance is required to achieve accurate target reaching in clinical applications. In this paper, a shape memory alloy actuated active flexible needle controlled by an adaptive sliding mode controller is presented. The trajectory tracking performance of the needle is tested while having its actual movement in an artificial tissue phantom by giving various input reference trajectories such as multi-step and sinusoidal. Performance of the adaptive sliding mode controller is compared with that of the proportional, integral and derivative controller and is proved to be the effective method in the presence of the external disturbances.

Design and evaluation of shape memory alloy‐actuated active needle using finite element analysis and deflection tracking control in soft tissues

“…The active needle's performance in tracking the reference trajectories was evaluated by measuring root mean squared error (RMSE) using Equation (). RMSE has been used as a standard measure to assess the controller's performance in tracking step and sinusoidal reference signals in the SMA actuators 55 and active devices built with SMA actuators 32 $$${PV}_{y}(t)=\sqrt{{(y(t)-y(0))}^{2}}$$$ $$${e}_{y}(t)={SP}_{y}(t)-{PV}_{y}(t)$$$ $$${u}_{y}(t)={K}_{c}{e}_{y}(t)+\frac{{K}_{c}}{{\tau }_{i}}\int \nolimits_{0}^{t}{e}_{y}(t)dt$$$ $$$RMSE=\sqrt{\frac{1}{n}\sum\limits _{i}{\left({{e}_{y}}_{i}\right)}^{2}}$$$ …”

“…RMSE has been used as a standard measure to assess the controller's performance in tracking step and sinusoidal reference signals in the SMA actuators 55 and active devices built with SMA actuators. 32 PV y ðtÞ ¼ ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffiffi ðyðtÞ − yð0ÞÞ 2…”

“…These material models have been built into commercial finite element software using subroutines 26 and applied in the simulation of smart devices like aerospace structures, 27 soft morphing actuators, 28 and arterial stents. 29 This study used a built-in Lagoudas material model 30 32 compared the needle tip trajectory tracking performance using electromagnetic (EM) position, vision, and ultrasound (US) feedback. They concluded that the EM sensor was the best to achieve a smaller root mean square error (RMSE).…”

“…Unlike temperature‐based control of SMA‐actuated needles, position feedback‐based control does not require constitutive SMA material models, which are computationally expensive. Joseph et al 32 . compared the needle tip trajectory tracking performance using electromagnetic (EM) position, vision, and ultrasound (US) feedback.…”

Analysis, optimization and testing of morphing structures employing shape memory alloy actuators

“…The composite controller was tuned through experimentation and its outcome was compared to a simple PID controller demonstrating its higher accuracy both numerically and experimentally. Maria Joseph & Tarun (2018) developed an adaptive sliding mode controller to track the motion of an SMA actuated active flexible needle. In the basis of the control system, a PID controller commanded the power supplied to the SMA actuated needle while an adaptive law was implemented to tune the PID gains.…”