Data-driven predictive control of exoskeleton for hand rehabilitation with subspace identification - AKGÜN, GAZİ (My University, 2022)
This study proposed a control method, a data-driven predictive control (DDPC), for the hand exoskeleton used for active, passive, and resistive rehabilitation. DDPC is a model-free approach based on past system data. One of the strengths of DDPC is that constraints of states can be added to the controller while performing the controller design. These features of the control algorithm eliminate an essential problem for rehabilitation robots in terms of easy customization and safe repetitive rehabilitation tasks that can be planned within certain constraints. Experiments were carried out with a designed hand rehabilitation system under repetitive and various therapy tasks. Real-time experiment results demonstrate the feasibility and efficiency of the proposed control approach to rehabilitation systems.
Keywords: Bilgi Sistemleri, Haberleşme ve Kontrol Mühendisliği; Sinyal İşleme; Kimya; Analitik Kimya; Temel Bilimler; Mühendislik ve Teknoloji; Information Systems, Communication and Control Engineering; Signal Processing; Chemistry; Analytical Chemistry; Natural Sciences; Engineering and Technology; KİMYA, ANALİTİK; Temel Bilimler (SCI); MÜHENDİSLİK, ELEKTRİK VE ELEKTRONİK; Mühendislik; Mühendislik, Bilişim ve Teknoloji (ENG); ALETLER & GÖSTERİM; CHEMISTRY, ANALYTICAL; CHEMISTRY; Natural Sciences (SCI); ENGINEERING, ELECTRICAL & ELECTRONIC; ENGINEERING; Engineering, Computing & Technology (ENG); INSTRUMENTS & INSTRUMENTATION; Enstrümantasyon; Genel Mühendislik; Elektrik ve Elektronik Mühendisliği; Mühendislik (çeşitli); Kimya (çeşitli); Genel Kimya; Filtrasyon ve Ayırma; Fizik Bilimleri; Instrumentation; General Engineering; Electrical and Electronic Engineering; Engineering (miscellaneous); Chemistry (miscellaneous); General Chemistry; Filtration and Separation; Physical Sciences; DDPC; hand rehabilitation; subspace identification; DESIGN; SYSTEM