Fault tolerant approach for biosignal-based robot control

Adaptive neural fault-tolerant prescribed performance control of a rehabilitation exoskeleton for lower limb passive training

This article studies the passive tracking problem of a wearable exoskeleton for lower limb rehabilitation therapy in the face of unmodeled dynamics, interactive friction, disturbance, prescribed performance constraints, and actuator faults. Adaptive neural networks and a smooth performance function are incorporated to establish a novel fault-tolerant tracking scheme, which can not only compensate for the nonlinear uncertainties and disturbance, but also handle the actuator fault with guaranteed tracking performance. A state feedback controller is presented by using the full state information and an output feedback controller is developed when the angular velocity is unavailable. The differential explosion issue of the backstepping technique is resolved by constructing a first-order filter and the unmeasurable velocity is estimated by a nonlinear observer. Semiglobal uniform boundedness stabilities of the exoskeleton system are proved via the Lyapunov direct method. The tracking performances of the designed control approaches are tested by comparative simulations.

Recurrent Neural Network for Contaminant Type Detector in Surface Electromyography Signals

A surface Electromyography (sEMG) contaminant type detector has been developed by using a Recurrent Neural Network (RNN) with Long Short-Term (LSMT) units in its hidden layer. This setup may reduce the contamination detection processing time since there is no need for feature extraction so that the classification occurs directly from the sEMG signal. The publicly available NINAPro (Non-Invasive Adaptive Prosthetics) database sEMG signals was used to train and test the network. Signals were contaminated with White Gaussian Noise, Movement Artifact, ECG and Power Line Interference. Two out of the 40 healthy subjects' data were considered to train the network and the other 38 to test it. Twelve models were trained under a -20dB contamination, one for each channel. ANOVA results showed that the training channel could affect the classification accuracy if SNR = -20dB and 0dB. An overall accuracy of 97.72% has been achieved by one of the models.