Bounded Control of an Actuated Lower-Limb Exoskeleton

Abstract

A bounded control strategy is employed for the rehabilitation and assistance of a patient with lower-limb disorder. Complete and partial lower-limb motor function disorders are considered. This application is centered on the knee and the ankle joint level, thereby considering a user in a sitting position. A high gain observer is used in the estimation of the angular position and angular velocities which is then applied to the estimation of the joint torques. The level of human contribution is feedback of a fraction of the estimated joint torque. This is utilised in order to meet the demands for a bounded human torque; that is, ℎ ≤ 2, ≤ 1, . The asymptotic stability of the bounded control law without human contribution and the convergence analysis of the high gain observer is verified using Lyapunov-based analysis. Simulations are performed to verify the proposed control law. Results obtained guarantee a fair trajectory tracking of the physiotherapist trajectory.

Robust LQR-Based Neural-Fuzzy Tracking Control for a Lower Limb Exoskeleton System with Parametric Uncertainties and External Disturbances

“…Although the exoskeleton systems exploit the gait of healthy humans to replicate the same using predefined trajectory control schemes, however, in practice, they are unable to attain the proper gait trajectory because of the parametric uncertainties and external disturbances (PUEDs). Therefore, various robust control strategies have been designed to deal with the limitations of classical trajectory tracking control in lower limb exoskeleton systems [ 18 , 19 , 20 , 21 , 22 , 23 ]. Ajayi et al [ 18 ] proposed a bounded control scheme for the rehabilitation of the knee ankle joint of a user in a sitting position.…”

“…Therefore, various robust control strategies have been designed to deal with the limitations of classical trajectory tracking control in lower limb exoskeleton systems [ 18 , 19 , 20 , 21 , 22 , 23 ]. Ajayi et al [ 18 ] proposed a bounded control scheme for the rehabilitation of the knee ankle joint of a user in a sitting position. The stability of the control law and convergence analysis of the gain observer is validated with the Lyapunov theory.…”

Optimal Linear Quadratic Control for Knee-Ankle Orthosis System

“…It consists of two actuators, one in each joint. This model takes into consideration the flexion/extension of the knee joint and the plantar-flexion/dorsal-flexion of the ankle joint, according to the motions are achieved in a sagittal plane due to the fact exoexercising tasks for lower limbs are the ones of sagittal plane movements [10]. The Lagrange equations are one of the feasible methods to derive the equations of motion.…”

Model‐free based adaptive finite time control with multilayer perceptron neural network estimation for a 10 DOF lower limb exoskeleton

“…Some of these control laws are based on artificial intelligence tools such as neural networks, [12][13][14][15] fuzzy logic, 16 neuron-fuzzy, 17,18 sliding modes, 13,[19][20][21][22] Kalman filters, 23 backstepping, 13,[24][25][26] and PID. 27 Despite significant progress in exoskeleton research, several challenges are still encountered during their operation. 12 One of the main difficulties is the highly complex nonlinear dynamics of the human-machine system (exoskeleton + body), which vary depending on the number of degrees of freedom (DOF) and the existence of uncertainties such as disturbances, noise, and imperfections, particularly during movement.…”