# A hierarchical Lyapunov-based cascade adaptive control scheme for lower-limb exoskeleton

## TL;DR

This paper introduces a hierarchical Lyapunov-based adaptive cascade control scheme for lower-limb exoskeletons, effectively managing uncertainties, nonlinearities, and control saturation to reduce interaction torque and improve control accuracy.

## Contribution

It presents a novel two-level control structure combining back-stepping and neural network adaptive control for exoskeletons, addressing model uncertainties and control saturation.

## Key findings

- Effective in reducing interaction torque compared to PD control
- Capable of handling model uncertainties and nonlinearities
- Demonstrated robustness under input constraints

## Abstract

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.