  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. We demonstrate the performance of the presented regulator on ATALANTE,
a fully actuated lower limb exoskeleton designed for paraplegic patients. The
simulation results show that the proposed approach noticeably improves
stability and the tracking performance of the system, despite significant
changes in model parameters and large adversarial pushes.
