Department of Instrumentation and Control Engineering, National Institute of Technology, Tiruchirappalli 620015, Tamil Nadu, India.
Department of Mechanical Engineering, The State University of New York, Korea (SUNY Korea), Incheon 21985, Republic of Korea.
Sensors (Basel). 2023 Feb 22;23(5):2442. doi: 10.3390/s23052442.
Self-sensing actuation of shape memory alloy (SMA) means to sense both mechanical and thermal properties/variables through the measurement of any internally changing electrical property such as resistance/inductance/capacitance/phase/frequency of an actuating material under actuation. The main contribution of this paper is to obtain the stiffness from the measurement of electrical resistance of a shape memory coil during variable stiffness actuation thereby, simulating its self-sensing characteristics by developing a Support Vector Machine (SVM) regression and nonlinear regression model. Experimental evaluation of the stiffness of a passive biased shape memory coil (SMC) in antagonistic connection, for different electrical (like activation current, excitation frequency, and duty cycle) and mechanical input conditions (for example, the operating condition pre-stress) is done in terms of change in electrical resistance through the measurement of the instantaneous value. The stiffness is then calculated from force and displacement, while by this scheme it is sensed from the electrical resistance. To fulfill the deficiency of a dedicated physical stiffness sensor, self-sensing stiffness by a Soft Sensor (equivalently SVM) is a boon for variable stiffness actuation. A simple and well-proven voltage division method is used for indirect stiffness sensing; wherein, voltages across the shape memory coil and series resistance provide the electrical resistance. The predicted stiffness of SVM matches well with the experimental stiffness and this is validated by evaluating the performances such as root mean squared error (RMSE), the goodness of fit and correlation coefficient. This self-sensing variable stiffness actuation (SSVSA) provides several advantages in applications of SMA: sensor-less systems, miniaturized systems, simplified control systems and possible stiffness feedback control.
自感知形状记忆合金 (SMA) 致动意味着通过测量驱动材料的任何内部变化的电特性来感测机械和热特性/变量，例如电阻/电感/电容/相/频率。本文的主要贡献是通过测量变刚度致动过程中形状记忆线圈的电阻来获得刚度，从而通过开发支持向量机 (SVM) 回归和非线性回归模型来模拟其自感知特性。通过测量瞬时值来评估被动偏置形状记忆线圈 (SMC) 在对抗连接中的刚度，针对不同的电输入条件（例如激活电流、激励频率和占空比）和机械输入条件（例如工作条件预紧力），通过测量电阻来评估电输入条件下的刚度变化。然后从力和位移计算刚度，而通过该方案从电阻感测刚度。为了弥补专用物理刚度传感器的不足，通过软传感器（等效地 SVM）进行自感测刚度是变刚度致动的福音。使用简单且经过充分验证的分压方法进行间接刚度感测；其中，形状记忆线圈和串联电阻两端的电压提供电阻。SVM 的预测刚度与实验刚度吻合良好，通过评估均方根误差 (RMSE)、拟合优度和相关系数等性能来验证这一点。这种自感知变刚度致动 (SSVSA) 在 SMA 的应用中具有许多优势：无传感器系统、小型化系统、简化的控制系统和可能的刚度反馈控制。
