The DOI matches its Crossref record; 32 of the 33 references it cites were found in a registry or an index.
Hybrid design of PID controller for four DoF lower limb exoskeleton
Open accessAbstract
Abstract In this paper, a method of tuning a proportional-integral-derivative controller for a four degree-of-freedom lower limb exoskeleton using hybrid of genetic algorithm and particle swarm optimization is presented. Transfer function of each link of the lower limb exoskeleton acquired from a pendulum model, was used in a closed-loop proportional-integral-derivative control system, while each link was assumed as one degree-of-freedom linkage. In the control system, the hybrid algorithm was applied to acquire the parameters of the controller for each joint for minimizing the error. The algorithm started with genetic algorithm and continued via particle swarm optimization. Furthermore, a 3-dimensional model of the lower limb exoskeleton was simulated to validate the proposed controller. The trajectory of the control system with optimized proportional-integral-derivative controller via hybrid precisely follows the input signal of the desired. The result of the hybrid optimized controller was compared with genetic algorithm and particle swarm optimization based on statistics. The average error of the proposed algorithm showed the optimized results in comparison with genetic algorithm and particle swarm optimization. Furthermore, the advantages of the hybrid algorithm have been indicated by numerical analysis.
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“The linear control algorithms, which are developed using conventional PID [11-14] or feedback linearization techniques [15,16], are simple and easy to implement in engineering. However, they only perform well under certain operating conditions, and fail to achieve satisfactory performance in the presence of aforementioned uncertainties, including parameter permutations, nonlinearities and disturb”
From Compensation Function Observer-Based Backstepping Sliding-Mode Control of Uncertain Electro-Hydraulic Servo System (2024) · cites this paper as [14]
Citations
Cites
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Neural Network Control of a Rehabilitation Robot by State and Output Feedback
He, W., Ge, S. S., Li, Y., Chew, E., & Ng, Y. S. (2015) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-014-0150-6 · Cited by 258
Adaptive Impedance Control of a Robotic Orthosis for Gait Rehabilitation
Hussain, S., Xie, S. Q., & Jamwal, P. K. (2013) · IEEE Transactions on Cybernetics · doi:10.1109/tsmcb.2012.2222374 · Cited by 223
Shunmugapriya, P., & Kanmani, S. (2017) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2017.04.002 · Cited by 216
ros_control: A generic and simple control framework for ROS
Chitta, S., Marder-Eppstein, E., Meeussen, W., Pradeep, V., Rodríguez Tsouroukdissian, A., Bohren, J., Coleman, D., Magyar, B., Raiola, G., Lüdtke, M., et al. (2017) · The Journal of Open Source Software · doi:10.21105/joss.00456 · Cited by 209
The design and control of a 3DOF lower limb rehabilitation robot
Wu, J., Gao, J., Song, R., Li, R., Li, Y., & Jiang, L. (2015) · Mechatronics · doi:10.1016/j.mechatronics.2015.11.010 · Cited by 144
Design of an active one-degree-of-freedom lower-limb exoskeleton with inertia compensation
Aguirre-Ollinger, G., Colgate, J. E., Peshkin, M. A., & Goswami, A. (2010) · The International Journal of Robotics Research · doi:10.1177/0278364910385730 · Cited by 140
Research works printed but not linked: 1
- Baluch, 2012, 6, 812
Cited by
Feng, H., Song, Q., Ma, S., Ma, W., Yin, C., Cao, D., & Yu, H. (2022) · ISA Transactions · doi:10.1016/j.isatra.2021.12.044 · Cited by 230
Optimal fuzzy logic-based control strategy for lower limb rehabilitation exoskeleton
Sharma, R., Gaur, P., Bhatt, S., & Joshi, D. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107226 · Cited by 70
Zhao, J., Yang, T., Sun, X., Dong, J., Wang, Z., & Yang, C. (2021) · Mechatronics · doi:10.1016/j.mechatronics.2021.102554 · Cited by 52
Amiri, M. S., Ramli, R., Ibrahim, M. F., Wahab, D. A., & Aliman, N. (2020) · Mathematics · doi:10.3390/math8112040 · Cited by 50
“The summation of the physical effect of each lower link such as mass and inertia is considered to be M i [24].”
BackgroundSelf-citationLiu, J., Fang, H., & Xu, J. (2021) · Machines · doi:10.3390/machines10010021 · Cited by 45
“For example, by employing the genetic algorithm and the particle swarm optimization (PSO) algorithm, an intelligent PID controller is proposed, which could effectively determine the PID gains for a 4-DOF lower extremity exoskeleton [36,37].”
Method
Metadata
- Authors
- Amiri, M. S., Ramli, R., & Ibrahim, M. F.
- Affiliations
- National University of Malaysia (MY)
- Corresponding author
- Ramli, Rizauddin
- Venue
- Applied Mathematical Modelling
- Publisher
- Elsevier BV
- Published
- 2019-08
- Volume
- 72
- Pages
- 17-27
- ISSN
- 0307-904X (ISSN-L)
- OpenAlex
- W2921821198
- Cited by
- 93
- References
- 33
- Citations by year
- 2026: 11 · 2025: 12 · 2024: 13 · 2023: 16 · 2022: 11 · 2021: 18 · 2020: 9 · 2019: 3
- FWCI
- 5.68
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 1461 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Applied Mathematical Modelling (publishedVersion)
- Funders
- Universiti Kebangsaan Malaysia
- Ministry of Higher Education, Malaysia (FRGS/1/2017/TK03/UKM/02/4)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Advanced Control Systems Design
- Physical Sciences › Engineering › Biomedical Engineering › Prosthetics and Rehabilitation Robotics
- Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
- Keywords
- PID controller tuning, lower limb exoskeleton, hybrid genetic algorithm and particle swarm optimization, controller tuning, feedback control, trajectory tracking, joint control
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- CoChat. (2026). Citation check: Hybrid design of PID controller for four DoF lower limb exoskeleton. https://app.cochat.ai/papers/10.1016/j.apm.2019.03.002
- The paper
- Amiri, M. S., Ramli, R., & Ibrahim, M. F. (2019). Hybrid design of PID controller for four DoF lower limb exoskeleton. Applied Mathematical Modelling. https://doi.org/10.1016/j.apm.2019.03.002
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
