Abdul-Hussain Gasak, Holderbaum William, Theodoridis Theodoros, Wei Guowu
School of Science, Engineering and Environment, University of Salford, Salford M5 4WT, UK.
Sensors (Basel). 2023 Aug 21;23(16):7293. doi: 10.3390/s23167293.
Soft tactile sensors based on piezoresistive materials have large-area sensing applications. However, their accuracy is often affected by hysteresis which poses a significant challenge during operation. This paper introduces a novel approach that employs a backpropagation (BP) neural network to address the hysteresis nonlinearity in conductive fiber-based tactile sensors. To assess the effectiveness of the proposed method, four sensor units were designed. These sensor units underwent force sequences to collect corresponding output resistance. A backpropagation network was trained using these sequences, thereby correcting the resistance values. The training process exhibited excellent convergence, effectively adjusting the network's parameters to minimize the error between predicted and actual resistance values. As a result, the trained BP network accurately predicted the output resistances. Several validation experiments were conducted to highlight the primary contribution of this research. The proposed method reduced the maximum hysteresis error from 24.2% of the sensor's full-scale output to 13.5%. This improvement established the approach as a promising solution for enhancing the accuracy of soft tactile sensors based on piezoresistive materials. By effectively mitigating hysteresis nonlinearity, the capabilities of soft tactile sensors in various applications can be enhanced. These sensors become more reliable and more efficient tools for the measurement and control of force, particularly in the fields of soft robotics and wearable technology. Consequently, their widespread applications extend to robotics, medical devices, consumer electronics, and gaming. Though the complete elimination of hysteresis in tactile sensors may not be feasible, the proposed method effectively modifies the hysteresis nonlinearity, leading to improved sensor output accuracy.
基于压阻材料的柔性触觉传感器具有大面积传感应用。然而，其精度常常受到滞后现象的影响，这在操作过程中构成了重大挑战。本文介绍了一种新颖的方法，该方法采用反向传播（BP）神经网络来解决基于导电纤维的触觉传感器中的滞后非线性问题。为了评估所提出方法的有效性，设计了四个传感器单元。这些传感器单元经历力序列以收集相应的输出电阻。使用这些序列训练反向传播网络，从而校正电阻值。训练过程表现出出色的收敛性，有效地调整了网络参数，以最小化预测电阻值与实际电阻值之间的误差。结果，经过训练的BP网络准确地预测了输出电阻。进行了几个验证实验以突出本研究的主要贡献。所提出的方法将最大滞后误差从传感器满量程输出的24.2%降低到了13.5%。这一改进使该方法成为提高基于压阻材料的柔性触觉传感器精度的一种有前景的解决方案。通过有效地减轻滞后非线性，可以增强柔性触觉传感器在各种应用中的能力。这些传感器成为用于力的测量和控制的更可靠、更高效的工具，特别是在软机器人技术和可穿戴技术领域。因此，它们的广泛应用扩展到机器人技术、医疗设备、消费电子产品和游戏领域。虽然完全消除触觉传感器中的滞后可能不可行，但所提出的方法有效地修正了滞后非线性，从而提高了传感器输出精度。
