Recurrent Neural Network with Finite Time Sampling for Dynamics Identification in Rehabilitation Robots

Rehabilitation robots can establish a direct connection between the user's nerve signals and the robot's actuators by integrating with the human nervous system. However, uncertainties in these systems limit their performance and accuracy. To address this challenge, the current study introduces an algorithm that effectively identifies and predicts unfamiliar dynamics in lower-limb rehabilitation robots. To accomplish this, the current study initially presents the dynamic model of a knee rehabilitation robot. Then, a finite time sampler is developed and the algorithm is proposed. In the proposed algorithm, the electromyographic signals are input into the rehabilitation robot. Via the use of a guaranteed stable sampler, samples from the unknown dynamics are extracted. By training the recurrent neural network with the acquired samples, the algorithm effectively learns and captures the underlying patterns of the unknown dynamics. The proposed recurrent neural network is enhanced with a self-attention mechanism, which plays a vital role in devising effective strategies for practical applications. Numerical simulation demonstrates the algorithm's effectiveness, highlighting its excellent performance in identifying the system's unknown dynamics.