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

Ali Nasr,Arash Hashemi,John Mcphee

doi:10.3390/robotics11010020

Abstract

The closed-loop human–robot system requires developing an effective robotic controller that considers models of both the human and the robot, as well as human adaptation to the robot. This paper develops a mid-level controller providing assist-as-needed (AAN) policies in a hierarchical control setting using two novel methods: model-based and fuzzy logic rule. The goal of AAN is to provide the required extra torque because of the robot’s dynamics and external load compared to the human limb free movement. The human–robot adaptation is simulated using a nonlinear model predictive controller (NMPC) as the human central nervous system (CNS) for three conditions of initial (the initial session of wearing the robot, without any previous experience), short-term (the entire first session, e.g., 45 min), and long-term experiences. The results showed that the two methods (model-based and fuzzy logic) outperform the traditional proportional method in providing AAN by considering distinctive human and robot models. Additionally, the CNS actuator model has difficulty in the initial experience and activates both antagonist and agonist muscles to reduce movement oscillations. In the long-term experience, the simulation shows no oscillation when the CNS NMPC learns the robot model and modifies its weights to simulate realistic human behavior. We found that the desired strength of the robot should be increased gradually to ignore unexpected human–robot interactions (e.g., robot vibration, human spasticity). The proposed mid-level controllers can be used for wearable assistive devices, exoskeletons, and rehabilitation robots.

Highlights

Received: 20 December 2021The human subject wearing a robotic assistive device that interacts with the environment forms a closed-loop system with two separate controllers: the human central nervous system (CNS) and the robot control system [1]
One solution to mitigate the vibration in the initial experience is using a lower desired strength Ω for the robot controller in the first moments of using the robot and increasing it after a while. This approach, i.e., changing the strength from less to high amount, has been experimentally evaluated in the literature [2,65,66], and the results have reported that the method of increasing the strength over time is preferred for human adaptation to robots
The controller gains and variables have been optimized for an AAN wearable robot during a task of free motion and lifting in the sagittal plane

Summary

Introduction

The human subject wearing a robotic assistive device that interacts with the environment forms a closed-loop system with two separate controllers: the human central nervous system (CNS) and the robot control system [1]. These two control systems simultaneously modify the behavior of the closed-loop system with different approaches of robot’s hierarchical control [2,3] and human’s optimal control [4]. The robot controller should be appropriately modified as the human adapts to the device to account for human–robot coadaptation For this purpose, researchers have either conducted human-in-the-loop studies or personalized computer simulations [1,6,7,8]. Since safety is a major consideration in biomechatronics research, this paper adopts the latter approach; the results of this research pave the way for future experimental implementations

Objectives

Results

Conclusion