Learning vector quantization neural network–based model reference adaptive control method for intelligent lower-limb prosthesis

Abstract

This article focuses on the design of a control system for intelligent prostheses. Learning vector quantization neural network-based model reference adaptive control method is employed to implement real-time trajectory tracking and damp torque control of intelligent lower-limb prosthesis. The method is then analyzed and proposed. A model reference control system is first built with two learning vector quantization neural networks. One neural network is used for output prediction, and the other is used for input control. The angle information of the prosthetic knee joint is utilized to train these two neural networks with the given learning algorithm. The testing results of different movement patterns verify the effectiveness of the proposed method and its suitability for intelligent lower-limb prostheses.

Neural network models for system identification, control and fault detection

“…In another work of neural-based MRAC, the successful control of marine vehicles is achieved by the use of a single layer neural network that bypasses the need for information about the system's dynamic structure and characteristics and provides portability (Leonessa, VanZwieten, and Morel, 2006). Also, a learning vector quantization neural network-based MRAC method is proposed to accomplish real-time trajectory tracking and damp torque control of an intelligent lower-limb prosthesis (Yang, Yang, and Ma, 2016).…”

“…The intuition behind the use of neural attention models is the human effort and ability to focus on certain regions of the visual image when trying to extract useful information and inference. Attention-based models have also been used for (Cho, Courville, and Bengio, 2015;Yang et al, 2016;Raffel and Ellis, 2015).…”