Control Algorithms in Robot-Assisted Rehabilitation: A Systematic Review
Source abstract
Abstract
Robotic-assisted rehabilitation has become an essential field in supporting the functional recovery of patients with neurological, musculoskeletal or post-traumatic conditions. This paper provides a systematic and applicative analysis of the control algorithms used in robotic rehabilitation systems, with a focus on the functional classification: position control, force, impedance, adaptive, artificial intelligence-based and hybrid schemes. The characteristics of each type of control, clinical applications, advantages and technical limitations are discussed in detail, illustrated by block diagrams and comparative graphs. The paper also includes a synthesis of existing commercial systems, a multi-criteria evaluation of the performance of the algorithms and an analysis of emerging trends in the recent literature (2020–2024). Current challenges regarding sensor integration, system standardization, real-time clinical feasibility and the applicability of brain–machine interfaces or adaptive myoelectric prostheses are discussed. The results obtained can support the development of efficient, safe and personalized solutions in the field of robotic rehabilitation.
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Research context
OpenAlex topics: Stroke Rehabilitation and Recovery · Prosthetics and Rehabilitation Robotics · Muscle activation and electromyography studies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Mechatronics and Machine Dynamics, Faculty of Automotive, Mechatronics and Mechanical Engineering, Technical University of Cluj Napoca, 400114 Cluj-Napoca, Romania
Funding
IOSUD
References (100+)
Show source-provided references
1. The Global Burden of Neurological Disorders: Translating Evidence into Policy10.1016/S1474-4422(19)30411-9 ↗
2. Global Estimates of the Need for Rehabilitation Based on the Global Burden of Disease Study 201910.1016/S0140-6736(20)32340-0 ↗
3. A Survey on Robotic Devices for Upper Limb Rehabilitation10.1186/1743-0003-11-3 ↗
4. Wearable Robotic Devices for Rehabilitation and Assistance: Design Issues and Technological Challenges
5. Effects of Robot-Assisted Therapy for the Upper Limb After Stroke: A Systematic Review and Meta-Analysis10.1177/1545968316666957 ↗
6. Upper Limb Robotic Rehabilitation After Stroke: A Multicenter, Randomized Clinical Trial10.1097/NPT.0000000000000295 ↗
7. Review of Control Strategies for Robotic Movement Training after Neurologic Injury10.1186/1743-0003-6-20 ↗
8. Ji, G., Zhou, Y., Yu, H., Wang, J., and Zhang, X. (2022). Human–Robot Interaction: A Review and Analysis on Variable Admittance Control and Safety in Physical HRI. Machines, 10.10.3390/machines10070591 ↗
9. Mahfouz, D.M., Shehata, O.M., Morgan, E.I., and Arrichiello, F. (2024). A Comprehensive Review of Control Challenges and Methods in End-Effector Upper-Limb Rehabilitation Robots. Robotics, 13.10.3390/robotics13120181 ↗
10. Radder, E.L., Meinhardt, U., Van Meulen, F., and Dederer, H. (2021). Safety Assessment of Rehabilitation Robots: A Review Identifying Safety Skills and Gaps. Front. Robot. AI, 8.10.3389/frobt.2021.602878 ↗
11. Chen, X., Li, Y., Wang, Z., and Zhang, M. (2023). Research on Control Strategy Technology of Upper Limb Exoskeleton Robots: A Review. Machines, 13.
12. Alam, N., Hasan, S., Mashud, G.A., and Bhujel, S. (2025). Neural Network for Enhancing Robot-Assisted Rehabilitation: A Systematic Review. Actuators, 14.10.3390/act14010016 ↗
13. Zhang, Y., Li, X., and Wang, J. (2023). Research on adaptive impedance control technology of upper limb rehabilitation robots. Front. Bioeng. Biotechnol., 11.10.3389/fbioe.2023.1332689 ↗
14. Impedance Modulation Control of a Lower-Limb Exoskeleton to Assist Sit-to-Stand Movements10.1109/TRO.2021.3104244 ↗
15. Zhang, H., Li, S., Zhong, J., Luo, S., Lei, M., and Wang, H. (2021). Design and Implementation of an Impedance-Based Assist-As-Needed Controller for an End-Effector Upper-Limb Exoskeleton. Appl. Sci., 11.
16. Liu, H., Xie, S., and Zhao, Y. (2022). Model Predictive Control for Soft Robotic Manipulators: A Review. Sensors, 22.
17. Robot-Aided Neurorehabilitation: A Pilot Clinical Trial with MIT-MANUS10.1109/86.662623 ↗
18. Chen, J., Hochstein, J., Kim, C., Tucker, L., Hammel, L.E., Damiano, D.L., and Bulea, T.C. (2021). A Pediatric Knee Exoskeleton with Real-Time Adaptive Control for Overground Walking in Ambulatory Individuals with Cerebral Palsy. Front. Robot. AI, 8.10.3389/frobt.2021.702137 ↗
19. Li, F., Guo, Y., Xu, W., Zhang, W., Zhao, F., Wang, B., Du, H., and Zhang, C. (2024). Design and control of a low-cost non-backdrivable end-effector upper limb rehabilitation device. Front. Rehabil. Sci., 5.10.3389/fresc.2024.1469491 ↗
20. Rosales-Luengas, Y., Salazar, S., Rangel-Popoca, S.J., Cortés-García, Y., Flores, J., and Lozano, R. (2025). Active Gait Retraining with Lower Limb Exoskeleton Based on Robust Force Control. Appl. Sci., 15.10.3390/app15074032 ↗
21. Brunet, M., Pétriaux, M., Di Meglio, F., and Petit, N. (2022). Fast replanning of a lower-limb exoskeleton trajectories for rehabilitation. arXiv.10.1109/CDC51059.2022.9992460 ↗
22. Diego, P., Herrero, S., Macho, E., Corral, J., Diez, M., Campa, F.J., and Pinto, C. (2024). Devices for Gait and Balance Rehabilitation: General Classification and a Narrative Review of End Effector-Based Manipulators. Appl. Sci., 14.10.3390/app14104147 ↗
23. Cenciarini, M., and Dollar, A.M. (July, January 29). Biomechanical Considerations in the Design of Lower-Limb Exoskeletons. Proceedings of the 2011 IEEE International Conference on Rehabilitation Robotics, Zurich, Switzerland.10.1109/ICORR.2011.5975366 ↗
24. Atkinson, M., Tully, A., Maher, C.A., Innes-Wong, C., Russo, R.N., and Osborn, M.P. (2023). Safety, Feasibility and Efficacy of Lokomat® and Armeo®Spring Training in Deconditioned Paediatric, Adolescent and Young Adult Cancer Patients. Cancers, 15.10.3390/cancers15041250 ↗
25. Potašová, M., Mačej, P., Moraučíková, E., Baňárová, P.S., and Kutiš, P. (2025). Lokomat vs. Conventional Therapy—Impact on Gait Symmetry in Hemiparetic Patients: Preliminary Clinical Study. Healthcare, 13.10.3390/healthcare13080929 ↗
26. Optimization of Joint Space Trajectories for Assistive Lower Limb Exoskeleton Robots: Real-Time and Flexible Gait Patterns10.1007/s10846-024-02137-9 ↗
27. The state of the art of soft robotics to assist mobility: A review of physiotherapist and patient identified limitations of current lower-limb exoskeletons and the potential soft-robotic solutions10.1186/s12984-022-01122-3 ↗
28. Overground Body Weight Support Systems for Gait and Balance Training: A Scoping Review
29. Veneman, J., Wessels, M., and Kloosterman, M. (2020). Development of the RYSEN: A 3D Body Weight Support System. Robotics, 9.
30. A Scoping Review on Lower Limb Exoskeleton Actuations: Description and Characteristics10.1017/S0263574725000220 ↗
31. Godage, I.S., Chen, Y., and Walker, I.D. (2018). Dynamic Control of Pneumatic Muscle Actuators. arXiv.
32. One Novel Hydraulic Actuating System for the Lower-Body Exoskeleton10.1186/s10033-021-00535-w ↗
33. Shi, K., Song, A., Li, Y., Chen, D., and Zhu, L. (2021). A Cable-Driven Three-DOF Wrist Rehabilitation Exoskeleton With Improved Performance. Front. Neurorobot., 15.10.3389/fnbot.2021.664062 ↗
34. Collision Avoidance Based on Null-Space Impedance Control for Upper Limb Rehabilitation Robots10.1007/s11517-023-02850-x ↗
35. Arantes, A., Bressan, N., Borges, L.R., and McGibbon, C.A. (2023). Evaluation of a Novel Real-Time Adaptive Assist-As-Needed Controller for Robot-Assisted Upper Extremity Rehabilitation Following Stroke. PLoS ONE, 18.10.1371/journal.pone.0292627 ↗
36. Luo, S., Huang, H., Li, X., Chen, J., and Si, J. (2021). Reinforcement learning-based motion control for a lower extremity rehabilitation exoskeleton performing collaborative squatting with unstable human-robot interaction. Front. Robot. AI, 8.10.3389/frobt.2021.702845 ↗
37. Advancing Biomedical Engineering through a Multi-Modal Sensor Fusion System for Enhanced Physical Training10.3934/bioeng.2023022 ↗
38. Qu, Z., Tang, W., Li, J., and Zhang, X. (2025). Fuzzy Adaptive Control for a 4-DOF Hand Rehabilitation Robot in Passive Mode. Actuators, 14.10.3390/act14070351 ↗
39. Olczak, A., Truszczyńska-Baszak, A., and Stępień, A. (2022). The Use of Armeo®Spring Device to Assess the Effect of Trunk Stabilization Exercises on the Functional Capabilities of the Upper Limb—An Observational Study of Patients after Stroke. Sensors, 22.10.3390/s22124336 ↗
40. Ma, J., Chen, H., Liu, X., Yang, Y., and Huang, D. (2025). Adaptive Impedance Control of a Human–Robotic System Based on Motion Intention Estimation and Output Constraints. Appl. Sci., 15.10.3390/app15031271 ↗
41. Park, C., and Kim, M.-S. (2024). Stratified predictions of upper limb motor outcomes after stroke. Front. Neurol., 14.10.3389/fneur.2023.1323529 ↗
42. Extended State Observer-Based Nonlinear Terminal Sliding Mode Control with Feed-forward Compensation for Lower Extremity Exoskeleton
43. Wang, R., Lin, X., Yin, C., Liu, Z., Zhang, Y., Liu, W., and Du, F. (2025). Robust Continuous Sliding-Mode-Based Assistive Torque Control for Series Elastic Actuator-Driven Hip Exoskeleton. Actuators, 14.10.3390/act14050239 ↗
44. Development of a Sliding Mode Controller with Chattering Suppressor for Human Lower Extremity Exoskeleton Robot10.1016/j.rico.2022.100123 ↗
45. Kim, J., Oh, S., Jo, Y., Moon, J.H., and Kim, J. (2023). A Robotic Treadmill System to Mimic Overground Walking Training with Body Weight Support. Front. Neurorobot., 17.10.3389/fnbot.2023.1089377 ↗
46. Laszlo, C., Munari, D., Maggioni, S., Knechtle, D., Wolf, P., and De Bon, D. (2023). Feasibility of an Intelligent Algorithm Based on an Assist-as-Needed Controller for a Robot-Aided Gait Trainer (Lokomat) in Neurological Disorders: A Longitudinal Pilot Study. Brain Sci., 13.10.3390/brainsci13040612 ↗
47. Design and Force Control of an End-Effector-Based Upper-Limb Rehabilitation Robot
48. Wang, Z., Liu, X., Chen, F., Zhang, Y., Li, J., and Sun, H. (2023). Adaptive Force Control With Kalman Filtering for Robotic Upper-Limb Rehabilitation. Sensors, 23.
49. A novel zero-force control framework for post-stroke rehabilitation training based on fuzzy-PID method10.20517/ir.2024.08 ↗
50. Development of a Force-Field-Based Control Strategy for an Upper-Limb Rehabilitation Robot10.5194/ms-13-949-2022 ↗
51. Chang, Y., Liu, X., Wang, L., Zhao, L., Sun, H., and Zhang, Y. (2023). Hybrid Position-Force Control in Cable-Driven Knee Exoskeletons. Sensors, 23.
52. Zhang, J., and Sun, J. (2023). Hybrid Control Strategy for Lower-Limb Rehabilitation Robot Combining Gravity Compensation and Feedforward Force. Appl. Sci., 13.
53. Fang, Y., Li, H., Zhang, X., Zhou, W., Yang, M., and Sun, H. (2021). Human–Robot Collaborative Control Based on Impedance Modulation with KUKA LWR for Post-Stroke Rehabilitation. Sensors, 21.
54. EMG-Driven Machine Learning Control of a Soft Assistive Glove for Grasping Assistance and Rehabilitation10.1109/LRA.2021.3140055 ↗
55. Hu, Y., Zhang, C., Wang, L., Liu, M., Zhou, Y., and Zhao, J. (2023). Fuzzy-Adaptive Impedance Control with Online Stiffness Adjustment in Rehabilitation Robots. Appl. Sci., 13.
56. Qin, T., Wu, Z., Lin, H.-Y., Chen, Z., and Zhang, J. (2023). Impedance Control Stability Analysis Considering Networked Communication Delays. Sensors, 23.
57. Hu, J., Zhuang, Y., Meng, Q., and Yu, H. (2023). Active Training Control Method for Rehabilitation Robot Based on Fuzzy Adaptive Impedance Adjustment. Machines, 11.10.3390/machines11050565 ↗
58. Zhang, Y., Zhao, L., Yang, W., Liu, T., and Sun, H. (2024). Adaptive Impedance Control for Upper-Limb Exoskeletons Based on RLS and Stiffness Optimization. Sensors, 24.
59. Soltani-Zarrin, R., Pourkand, A., Kazemi, H., and Fattahi, N. (2024). A Dual-Port Admittance Control Scheme for Upper-Limb Rehabilitation Exoskeletons. Appl. Sci., 14.
60. Variable Admittance Control of High Compatibility Exoskeleton Based on Human–Robotic Interaction Force10.1186/s10033-024-01113-6 ↗
61. Mashud, G.A., Hasan, S.K., and Alam, N. (2025). Advances in Control Techniques for Rehabilitation Exoskeleton Robots: A Systematic Review. Actuators, 14.10.3390/act14030108 ↗
62. Peternel, L., Noda, T., Petrič, T., Ude, A., Morimoto, J., and Babič, J. (2016). Adaptive Control of Exoskeleton Robots for Periodic Assistive Behaviours Based on EMG Feedback Minimisation. PLoS ONE, 11.10.1371/journal.pone.0148942 ↗
63. Amiri, M.S., Ramli, R., and Barari, A. (2023). Optimally Initialized Model Reference Adaptive Controller of Wearable Lower Limb Rehabilitation Exoskeleton. Mathematics, 11.10.3390/math11071564 ↗
64. Exoskeleton rehabilitation robot training for balance and lower limb function in sub-acute stroke patients: A pilot, randomized controlled trial10.1186/s12984-024-01391-0 ↗
65. Robust Adaptive Backstepping Control for a Lower-Limb Exoskeleton System with Model Uncertainties and External Disturbances10.1080/00051144.2022.2119498 ↗
66. Ghannadi, B., Sharif Razavian, R., and McPhee, J. (2018). Configuration-Dependent Optimal Impedance Control of an Upper Extremity Stroke Rehabilitation Manipulandum. Front. Robot. AI, 5.10.3389/frobt.2018.00124 ↗
67. Sensor-Less Sliding Mode Observer-Based Assistive Control of a Spherical Parallel Ankle Rehabilitation Robot Under Modeling Uncertainties
68. Zhao, Z., Hou, X., Shan, D., Liu, H., Liu, H., and Hao, L. (2024). Application of Fuzzy Adaptive Impedance Control Based on Backstepping Method for PAM Elbow Exoskeleton in Rehabilitation. Polymers, 16.10.3390/polym16243533 ↗
69. Direct Continuous Electromyographic Control of a Powered Prosthetic Ankle for Improved Postural Control after Guided Physical Training: A Case Study10.1017/wtc.2021.2 ↗
70. Brain–Machine Interface Based on Deep Learning to Control Asynchronously a Lower-Limb Robotic Exoskeleton: A Case-of-Study10.1186/s12984-024-01342-9 ↗
71. A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI10.1109/TNNLS.2020.3027314 ↗
72. Robust Walking Control of a Lower Limb Rehabilitation Exoskeleton Coupled with a Musculoskeletal Model via Deep Reinforcement Learning10.1186/s12984-023-01147-2 ↗
73. Sambhus, R., Gokce, A., Welch, S., Herron, C.W., and Leonessa, A. (2023). Real-Time Model-Free Deep Reinforcement Learning for Force Control of a Series Elastic Actuator. arXiv.10.1109/IROS55552.2023.10341751 ↗
74. Kumar, R., Muthukrishnan, S.P., Kumar, L., and Roy, S. (2023). Predicting Multi-Joint Kinematics of the Upper Limb from EMG Signals Across Varied Loads with a Physics-Informed Neural Network. arXiv.
75. Duong, M.-D., Pham, Q.-T., Vu, T.-C., Bui, N.-T., and Dao, Q.-T. (2023). Adaptive Fuzzy Sliding Mode Control of an Actuator Powered by Pneumatic Artificial Muscles. Sci. Rep., 13.10.1038/s41598-023-34491-3 ↗
76. Model-Based Control of Soft Robots: A Survey of the State of the Art and Open Challenges10.1109/MCS.2023.3253419 ↗
77. Shi, Y., Dong, W., Lin, W., and Gao, Y. (2022). Soft Wearable Robots: Development Status and Technical Challenges. Sensors, 22.10.3390/s22197584 ↗
78. Zhao, W., and Song, A. (2020). Active Motion Control of a Knee Exoskeleton Driven by Antagonistic Pneumatic Muscle Actuators. Actuators, 9.10.3390/act9040134 ↗
79. Soft Robotic Glove for Combined Assistance and At-Home Rehabilitation10.1016/j.robot.2014.08.014 ↗
80. Model Predictive Control for Soft Ankle Rehabilitation Robot
81. Intelligent Upper-Limb Exoskeleton Integrated with Soft Wearable Bioelectronics and Deep Learning for Human Intention-Driven Strength Augmentation10.1038/s41528-024-00297-0 ↗
82. Design and Control of a Powered Transfemoral Prosthesis10.1177/0278364907084588 ↗
83. Li, Z., Fang, Y., Luo, Y., He, H., and Wang, X. (2022). A Survey on Myoelectric Control for Lower Limb Prostheses. Sensors, 22.
84. Neural prosthesis control restores near-normative neuromechanics in postural control10.1126/scirobotics.adf5758 ↗
85. Toward Developing a Powered Ankle-Foot Prosthesis with Electromyographic Control to Enhance Functional Performance: A Case Study in a U.S. Service Member
86. EEG-based Brain–Computer Interface Enables Real-time Robotic Hand Control at Individual Finger Level10.1038/s41467-025-61064-x ↗
87. A Comprehensive Review of EEG-Based Brain–Computer Interface Paradigms10.1088/1741-2552/aaf12e ↗
88. Śliwowski, W., Leroy, A., Hueber, T., Lainé, A., Fantenberg, M., Serre, A., Roche, N., Jutten, C., Mattout, J., and Guan, C. (2024). Adaptive LDA Classifier Enhances Real-Time Control of an EEG-Based Imagined Speech Brain–Computer Interface. Brain Sci., 14.
89. Cheron, G., Coenen, V., Grishin, A., Leurs, K., Lourenço, A.P., and Ivanenko, Y. (2021). MindWalker: BCI-Controlled Lower-Limb Exoskeleton for Gait Assistance. Brain Sci., 11.
90. Bundy, D.T., Pandarinath, C., Kirchner, J., Franklin, R.J., Hart, J., Tucker, R., Broderick, B., Botvinick, M.M., and Contreras-Vidal, J.L. (2020). BCI–Robot Hybrid Systems for Stroke Rehabilitation: Current Trends and Challenges. Front. Hum. Neurosci., 14.
91. Brain–Computer Interfaces in Medicine10.1016/j.mayocp.2011.12.008 ↗
92. Tramontano, M., Li, X., Miskovic, P., Ijspeert, A., and Gassert, R. (2021). Multimodal Sensor Integration in Robotic Neurorehabilitation: A Review. Sensors, 21.
93. An, Y., Li, Y., Wang, H., Duffield, R., and Su, S.W. (2024). Human–Machine Co-Adaptation for Robot-Assisted Rehabilitation via Dual-Agent Multiple-Model Reinforcement Learning. arXiv.
94. Development of an Upper Limb Exoskeleton for Rehabilitation with Feedback from EMG and IMU Sensor10.1016/j.procs.2015.12.275 ↗
95. Dissanayake, H., Wijesundara, P., Gunaratne, H., Samaranayake, T., and Perera, C. (2022). Sensor Fusion Framework Using EMG, IMU, and Visual Feedback in Upper-Limb Rehabilitation Robots. Sensors, 22.
96. Deep Learning–Based Multimodal Data Fusion: Case Study in Food Intake Episodes Detection Using Wearable Sensors10.2196/21926 ↗
97. Systematic Review of AI/ML Applications in Multi-Domain Robotic Rehabilitation: Trends, Gaps, and Future Directions10.1186/s12984-025-01605-z ↗
98. Banyai, A.D., and Brișan, C. (2024). Robotics in Physical Rehabilitation: Systematic Review. Healthcare, 12.10.20944/preprints202406.2007.v1 ↗
99. From Bench to Bedside: Translating Robotic Rehabilitation into Clinical Practice
100. OpenExo: An Open-Source Modular Exoskeleton to Augment Human Walking10.1126/scirobotics.adt1591 ↗