An Adaptive Assistance Controller to Optimize the Exoskeleton Contribution in Rehabilitation

Abstract

In this paper, we present a novel adaptation rule to optimize the exoskeleton assistance in rehabilitation tasks. The proposed method adapts the exoskeleton contribution to user impairment severity without any prior knowledge about the user motor capacity. The proposed controller is a combination of an adaptive feedforward controller and a low gain adaptive PD controller. The PD controller guarantees the stability of the human-exoskeleton system during feedforward torque adaptation by utilizing only the human-exoskeleton joint positions as the sensory feedback for assistive torque optimization. In addition to providing a convergence proof, in order to study the performance of our method we applied it to a simplified 2-DOF model of human-arm and a generic 9-DOF model of lower limb to perform walking. In each simulated task, we implemented the impaired human torque to be insufficient for the task completion. Moreover, the scenarios that violate our convergence proof assumptions are considered. The simulation results show a converging behavior for the proposed controller; the maximum convergence time of 20 s is observed. In addition, a stable control performance that optimally supplements the remaining user motor contribution is observed; the joint angle tracking error in steady condition and its improvement compared to the start of adaptation are as follows: shoulder 0.96±2.53° (76%); elbow −0.35±0.81° (33%); hip 0.10±0.86° (38%); knee −0.19±0.67° (25%); and ankle −0.05±0.20° (60%). The presented simulation results verify the robustness of proposed adaptive method in cases that differ from our mathematical assumptions and indicate its potentials to be used in practice.

Model-Based Mid-Level Regulation for Assist-As-Needed Hierarchical Control of Wearable Robots: A Computational Study of Human-Robot Adaptation

“…The idea behind the proposed mid-level controllers is to consider both the human and the robot models. Taking into consideration models of the human and the robot, as well as using a nonlinear controller (e.g., model-based, fuzzy-logic, impedance control [ 67 , 68 , 69 ], haptic/admittance control [ 30 , 70 ], and adaptive control [ 43 , 71 ]) improves the efficiency of AAN control of wearable robots during two different tasks of free motion and lifting. The nonlinear controllers should be prioritized over proportional controllers for controlling the nonlinear human limb model.…”

“…Instead of decreasing the strength of the mid-level controller, the threshold and the dead zone can be increased, but the strength can remain the same. A third possible solution to reducing the vibrations may be using an adaptive controller [43] or using a model predictive controller (MPC) with dual purposes of decreasing the joint error and the human-provided torque for the motion. The model-based and fuzzy-logic controllers are based on the joint angle and velocity.…”

“…Multiple research has postulated that the CNS obtains the optimal inputs by minimizing a cost function that can include jerk [ 38 ], torque [ 39 ], muscle activation [ 40 ], metabolic energy [ 41 ], and muscle fatigue [ 42 ] terms. However, in most mid-level controller assessment studies, the central neural system is modeled with a Proportional-Integral-Derivative (PID) controller [ 43 , 44 ]. The wearer can adapt to the robot dynamics and the controller in the long term, yet the adaptation level is low in the initial and short-term experiences.…”

An Approach to Investigate Public Opinion, Views, and Perspectives Towards Exoskeleton Technology

“…Li et al (Li et al 2021) developed a system that employed ordinal and preference feedback, as well as Bayesian posteriors, to estimate the utility landscape of users across four distinct exoskeleton gait parameters. In (Nasiri et al 2021), the authors developed an adaptive controller that optimized the support provided by an exoskeleton in rehabilitation tasks without prior knowledge of the user's motor capabilities. The approach employed sensory feedback and was applied to the degree of freedom models of the human arm and lower limb to study the overall performance of the system.…”

Design of Optimal Sliding Mode Control of Elbow Wearable Exoskeleton System Based on Whale Optimization Algorithm

“…In order for the system to work properly, it needs to take into account factors like how quickly and where each limb moves, how well it can adapt to its environment, and how much energy each limb needs. These are all factors that must be taken into account [6][7][8].…”