Real-time fuzzy adaptive FOPID controller design addressing compliant human–robot interaction in pneumatic rehabilitation robot with novel disturbance rejection approaches
JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING, cilt.2026, sa.48, ss.1-23, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 2026 Sayı: 48
- Basım Tarihi: 2026
- Doi Numarası: 10.1007/s40430-026-06652-8
- Dergi Adı: JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING
- Derginin Tarandığı İndeksler: Scopus, Materials Science & Engineering Collection (ProQuest), Technology Collection (ProQuest), Aerospace Database, Science Citation Index Expanded (SCI-EXPANDED), Compendex, INSPEC
- Sayfa Sayıları: ss.1-23
- Çukurova Üniversitesi Adresli: Evet
Özet
Rehabilitation robots are becoming indispensable tools for patients with varying levels of disability and physical condition. However, during their clinical utilization, unexpected disturbances can severely compromise control performance, compliance, and patient safety. To address these challenges, this study proposes a fuzzy adaptive fractional-order PID control framework for safe and compliant interaction torque regulation in a pneumatically actuated wrist/forearm rehabilitation robot performing pronation and supination exercises. The FOPID gains are adaptively tuned through a fuzzy logic mechanism to maintain optimal transient and steady-state performance under rapidly changing biomechanical and environmental conditions. In addition to adaptive control, a human motion intent detection algorithm is incorporated to enhance compliance and ensure intuitive, patient-driven interaction. Furthermore, dual-layer disturbance rejection algorithms are developed to accurately detect and actively compensate for both internal disturbances (e.g., pneumatic pressure losses) and external disturbances (e.g., user-induced torque perturbations) in real time. The proposed framework is validated through real-time hardware-in-the-loop (HIL) experiments under multiple disturbance scenarios and benchmarked against both conventional and fuzzy gain-scheduled FOPID controllers. The results demonstrate that the proposed approach significantly improves tracking accuracy, disturbance rejection capability, and safety compliance, highlighting its potential for fault-tolerant and clinically ready rehabilitation robotics.