Feedback error learning neural network for trans-femoral prosthesis

• Published in: IEEE transactions on neural systems and rehabilitation engineering 2000-03, Vol.8 (1), p.71-80
• Main Authors: Kalanovic, V.D., Popovic, D., Skaug, N.T.
• Format: Article
• Language: English
• Subjects: Activities of Daily Living; Artificial Limbs; Automatic control; Bias; Biomechanical Phenomena; Biomechanics; Control nonlinearities; Feedback; Finite Element Analysis; Humans; Joints (anatomy); Learning automata; Learning systems; Leg - physiopathology; Legged locomotion; Machine learning; Neural networks; Neural Networks (Computer); Neural prosthesis; Neurofeedback; Nonlinear Dynamics; Prostheses; Prosthesis Design; Range of Motion, Articular - physiology; Redundancy; Reproducibility of Results; Statistics, Nonparametric; Studies; Time Factors; Uncertainty; Walking - physiology
• ISSN: 1063-6528; 1534-4320; 1558-0024; 1558-0210
• DOI: 10.1109/86.830951

Feedback-error learning (FEL) neural network was developed for control of a powered trans-femoral prosthesis. Nonlinearities and time-variations of the dynamics of the plant, in addition to redundancy and dynamic uncertainty during the double support phase of walking, makes conventional control methods very difficult to use. Rule-based control, which uses a knowledge base determined by machine learning and finite automata method is limited since it does not respond well to perturbations and environmental changes. FEL can be regarded as a hybrid control, because it combines nonparametric identification with parametric modeling and control. This paper presents simulation of a powered trans-femoral prosthesis controlled by a FEL neural network. Results suggest that FEL can be used to identify inverse dynamics of an arbitrary trans-femoral prosthesis during simple single joint movements (e.g., sinusoidal oscillations). The identified inverse dynamics then allows the tracking of an arbitrary trajectory such as a desired walking pattern within a multijoint structure. Simulation shows that the identified controller responds correctly when the leg motion is exposed to a perturbation such as a frequent change of the ground reaction force or the hip joint torque generated by the user. FEL eliminates the need for precise, tedious, and complex identification of model parameters.