Adaptive Feedback Regulator for Powered Lower-Limb Exoskeleton under Model Uncertainty

Understand

This paper presents a neural network (NN) based adaptive feedback regulator to ensure the lateral and longitudinal stability and regulate the desired walking velocity of a lower-limb exoskeleton under model uncertainty.

• The traditional model-based controllers for lower-limb exoskeletons often fail to stabilize the robot or accurately track the desired behaviors under model uncertainties or external disturbances.
• This paper proposes a neural network (NN) based online adaptive regulator that compensates for the unknown changes in model parameters and external disturbances by modifying the nominal joint trajectory.
• A gradient descent-based delta rule is implemented to update the weights of a single layer NN, which can be efficiently performed online by design.