  This paper proposes a hierarchical Lyapunov-based adaptive cascade control
scheme for a lower-limb exoskeleton with control saturation. The proposed
approach is composed by two control levels with cascade structure. At the
higher layer of the structure, a Lyapunov-based back-stepping regulator
including adaptive estimation of uncertain parameters and friction force is
designed for the leg dynamics, to minimize the deviation of the joint position
and its reference value. At the lower layer, a Lyapunov-based neural network
adaptive controller is in charge of computing control action for the hydraulic
servo system, to follow the force reference computed at the high level, also to
compensate for model uncertainty, nonlinearity, and control saturation. The
proposed approach shows to be capable in minimizing the interaction torque
between machine and human, and suitable for possible imprecise models. The
robustness of the closed-loop system is discussed under input constraint.
Simulation experiments are reported, which shows that the proposed scheme is
effective in imposing smaller interaction torque with respect to PD controller,
and in control of models with uncertainty and nonlinearity.
