Cheng Longlong, Zhang Guangju, Wan Baikun, Hao Linlin, Qi Hongzhi, Ming Dong
Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, P. R. China.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:3481-4. doi: 10.1109/IEMBS.2009.5334566.
Functional electrical stimulation (FES) has been widely used in the area of neural engineering. It utilizes electrical current to activate nerves innervating extremities affected by paralysis. An effective combination of a traditional PID controller and a neural network, being capable of nonlinear expression and adaptive learning property, supply a more reliable approach to construct FES controller that help the paraplegia complete the action they want. A FES system tuned by Radial Basis Function (RBF) Neural Network-based Proportional-Integral-Derivative (PID) model was designed to control the knee joint according to the desired trajectory through stimulation of lower limbs muscles in this paper. Experiment result shows that the FES system with RBF Neural Network-based PID model get a better performance when tracking the preset trajectory of knee angle comparing with the system adjusted by Ziegler- Nichols tuning PID model.
功能性电刺激（FES）已在神经工程领域得到广泛应用。它利用电流来激活支配受瘫痪影响肢体的神经。传统PID控制器与具有非线性表达和自适应学习特性的神经网络的有效结合，为构建FES控制器提供了一种更可靠的方法，有助于截瘫患者完成他们想要的动作。本文设计了一种基于径向基函数（RBF）神经网络的比例积分微分（PID）模型进行调谐的FES系统，通过刺激下肢肌肉根据期望轨迹控制膝关节。实验结果表明，与采用齐格勒 - 尼科尔斯整定PID模型调节的系统相比，基于RBF神经网络PID模型的FES系统在跟踪膝关节角度预设轨迹时具有更好的性能。
