Feedback error learning neural network for trans-femoral prosthesis.
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.
Kalanovic
V D
VD
Department of Mechanical Engineering, South Dakota School of Mines and Technology, Rapid City 57701, USA.
Popovic
D
D
Skaug
N T
NT
D000203:Activities of Daily Living; D001186:Artificial Limbs; D015982:Bias; D001696:Biomechanical Phenomena; D005246:Feedback; D020342:Finite Element Analysis; D006801:Humans; D007866:Leg / Q000503:physiopathology*; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D011474:Prosthesis Design; D016059:Range of Motion, Articular / Q000502:physiology*; D015203:Reproducibility of Results; D018709:Statistics, Nonparametric; D013997:Time Factors; D016138:Walking / Q000502:physiology*