Citation: Amiri, M.S.; Ramli, R.;
Aliman, N. Adaptive Swarm Fuzzy
Logic Controller of Multi-Joint Lower
Limb Assistive Robot. Machines 2022,
10, 425. https://doi.org/10.3390/
machines10060425
Academic Editors: Shuai Li, Dechao
Chen, Mohammed Aquil Mirza,
Vasilios N. Katsikis, Dunhui Xiao and
Predrag Stanimirovi´c
Received: 24 April 2022
Accepted: 23 May 2022
Published: 27 May 2022
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Article
Adaptive Swarm Fuzzy Logic Controller of Multi-Joint Lower
Limb Assistive Robot
Mohammad Soleimani Amiri
1
, Rizauddin Ramli
1,
* and Norazam Aliman
2
1
Department of Mechanical and Manufacturing Engineering, Faculty of Engineering and Built Environment,
Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia; p90554@siswa.ukm.edu.my
2
Department of Mechanical Engineering, Politeknik Sultan Azlan Shah, Behrang 35950, Perak, Malaysia;
azam_justice_01@yahoo.com
* Correspondence: rizauddin@ukm.edu.my
Abstract:
The idea of developing a multi-joint rehabilitation robot is to satisfy the demands for
recovery of lower limb functionality in hemiplegic impairments and assist the physiotherapists
with their therapy plans. This work aims at to implement the Lyapunov Adaptive and Swarm-
Fuzzy Logic Control (LASFC) strategy of 4-degree of freedom (4-DoF) Lower Limb Assistive Robot
(LLAR) application, in which the control law is an integration of swarm-fuzzy logic control (SFLC)
and Lyapunov adaptive control (LAC) with particle swarm optimization (PSO). The controller is
established based on the sliding filtered steady-state error for SFLC. Its parameters are tuned by
using PSO for the mathematical model of LLAR. The fuzzy defuzzification membership is set based
on the tuned parameters for the real-time control system. LAC strategy is determined using stability
analysis of the system to choose the controller’s parameters by observation of the system’s output
and reference. The control law implemented in LLAR is the integration of SFLC and LAC to adjust
the input voltage of joints. The parameters tuned by PSO are compared with the genetic algorithm
(GA) statistically. In addition, the real-time trajectory tracking of the proposed controller for each joint
is compared with LAC and SFLC separately. The experiment revealed that the LASFC has superior
performance to the other two methods in trajectory tracking. For example, the average error for left
hip by LASFC is 53.57% and 68% lower than SFLC and LAC, respectively. By the statistical analysis,
it can be ascertained that the LASFC strategy performed efficiently for real-time control of the joint
trajectory tracking.
Keywords:
lower limb assistive robot; Lyapunov adaptive control; swarm-fuzzy logic controller;
particle swarm optimization
1. Introduction
The occurrence of neurological diseases such as spinal cord injuries and stroke have
increased and have become one of the main reasons for mobility impairments and vestibular
dysfunction in many countries [
1
–
3
]. Adequate rehabilitation training is essential to slow
down the disabling effects of chronic health conditions [
4
]. Recent clinical investigations on
neurological rehabilitation have revealed the positive influence of repetitive motor activities
on obtaining movement functionality. Traditional rehabilitation methods are physically
demanding work, in which physiotherapists are required to deliver manual treatment based
on their experiences. Practicing conventional manual therapy is often difficult and needs
considerable exertion by practitioners to provide care for large patients [
5
]. Therefore, the ap-
plication of robot-assisted rehabilitation devices has increased recently due to their potential
in providing repetitive rehabilitation training to avoid muscle atrophy for disabled patients
and lessen the physical efforts of the physiotherapists [
6
]. Integration of robotic technology
and clinical rehabilitation treatments provides affordable and efficient long-endurance treat-
ments as an alternative to high-intensity repetitive manual rehabilitation therapy. According
to previous work, robot-assisted systems have been investigated in the implementation
Machines 2022, 10, 425. https://doi.org/10.3390/machines10060425 https://www.mdpi.com/journal/machines
