Neural Network-Based Impedance Learning Controller With Anti-Noise Performance: Application to Prosthesis Finger

The prosthetic finger demands an impedance controller with outstanding control performance to satisfy the tracking tasks’ requirements in complicated surroundings. For this purpose, suppressing the disturbances caused by the internal and external environments is the main concern for enhancing the universality of forming the impedance controller. This article presents a noise-tolerant zeroing neural network (NTZNN)-based impedance learning controller, which aims to strengthen the anti-noise performance and calculation accuracy of the prosthesis finger controller under noise pollution. Besides, the proposed impedance learning controller consists of an NTZNN model for calculating the actual trajectories in noisy circumstances, an adaptive learning law for improving the convergence property, and an interactive control term to enhance the transient performance. Furthermore, the stability and convergence performances are verified with a candidate Lyapunov function. In addition, the simulative and experimental results showcase the cutting-edge noise-tolerant, high calculation accuracy, and excellent long-term control property of the proposed controller under the noise pollution, which achieves the accuracy at the order of 10^-5 rad for position level and at the order of 10^-3 rad/s for velocity level. Eventually, in the noise environment, the accuracy of root-mean-square error (RMSE) and L₂-norm in position and velocity levels for utilizing the NTZNN-based learning impedance learning controller achieves 0.0056 rad, 0.0564 rad/s, 0.2217 rad, and 2.5216 rad/s, respectively.