Citation: Liu, J.; Fang, H.; Xu, J.
Online Adaptive PID Control for a
Multi-Joint Lower Extremity
Exoskeleton System Using Improved
Particle Swarm Optimization.
Machines 2022, 10, 21. https://
doi.org/10.3390/machines10010021
Academic Editor: Jan Awrejcewicz
Received: 8 November 2021
Accepted: 22 December 2021
Published: 27 December 2021
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Article
Online Adaptive PID Control for a Multi-Joint Lower Extremity
Exoskeleton System Using Improved Particle Swarm Optimization
Jiaqi Liu
1
, Hongbin Fang
2,3,4,
* and Jian Xu
1
1
School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai 200092, China;
1910726@tongji.edu.cn (J.L.); xujian@tongji.edu.cn (J.X.)
2
Institute of AI and Robotics, Fudan University, Shanghai 200433, China
3
MOE Engineering Research Center of AI & Robotics, Fudan University, Shanghai 200433, China
4
Shanghai Engineering Research Center of AI & Robotics, Fudan University, Shanghai 200433, China
* Correspondence: fanghongbin@fudan.edu.cn
Abstract:
Robotic exoskeletons have great potential in the medical rehabilitation and augmentation
of human performance in a variety of tasks. Proposing effective and adaptive control strategies is
one of the most challenging issues for exoskeleton systems to work interactively with the user in
dynamic environments and variable tasks. This research, therefore, aims to advance the state of the
art of the exoskeleton adaptive control by integrating the excellent search capability of metaheuristic
algorithms with the PID feedback mechanism. Specifically, this paper proposes an online adaptive PID
controller for a multi-joint lower extremity exoskeleton system by making use of the particle swarm
optimization (PSO) algorithm. Significant improvements, including a ‘leaving and re-searching
mechanism’, are introduced into the PSO algorithm for better and faster update of the solution and to
prevent premature convergence. In this research, a 9-DOF lower extremity exoskeleton with seven
controllable joints is adopted as a test-bench, whose first-principle dynamic model is developed,
which includes as many uncertain factors as possible for generality, including human–exoskeleton
interactions, environmental forces, and joint unilateral constraint forces. Based upon this, to illustrate
the effectiveness of the proposed controller, the human–exoskeleton coupled system is simulated in
four characteristic scenarios, in which the following factors are considered: exoskeleton parameter
perturbations, human effects, walking terrain switches, and walking speed variations. The results
indicate that the proposed controller is superior to the standard PSO algorithm and the conventional
PID controller in achieving rapid convergence, suppressing the undesired chattering of PID gains,
adaptively adjusting PID coefficients when internal or external disturbances are encountered, and
improving tracking accuracy in both position and velocity. We also demonstrate that the proposed
controller could be used to switch the working mode of the exoskeleton for either performance or an
energy-saving consideration. Overall, aiming at a multi-joint lower extremity exoskeleton system,
this research proposes a PSO-based online adaptive PID controller that can be easily implemented in
applications. Through rich and practical case studies, the excellent anti-interference capability and
environment/task adaptivity of the controller are exemplified.
Keywords:
exoskeleton robot; model-free control; adaptive control; trajectory tracking control;
uncertainties and disturbances
1. Introduction
Powered robotic exoskeletons, according to their purposes, can be divided into the
following three categories: medical, industrial, and military. In the field of medical reha-
bilitation [
1
–
3
], the major objective of powered exoskeletons is to provide force assistance
for the elderly with muscle weakness caused by aging or to offer effective rehabilitation to
incapacitated patients. In industrial applications [
4
–
7
], exoskeleton technology is increas-
ingly used to assist workers with repetitive tasks and heavy loads for reducing working
Machines 2022, 10, 21. https://doi.org/10.3390/machines10010021 https://www.mdpi.com/journal/machines
