Adaptive iterative learning-based gait tracking control for paediatric exoskeleton during passive-assist rehabilitation. (17th April 2022)

• Record Type: Journal Article
• Authors: Narayan, Jyotindra; Abbas, Mohamed; Dwivedy, Santosha K.
• Is Part Of: International journal of intelligent engineering informatics. Volume 9:Number 6(2021)
• Page Start: 507
• Page End: 532
• Publication Date: 2022-04-17
• Subjects: robust control -- AIL -- adaptive iterative learning -- paediatric exoskeleton -- Lyapunov -- parametric uncertainties -- un-modelled disturbances; Artificial intelligence -- Engineering applications -- Periodicals; Engineering -- Computer programs -- Periodicals; Knowledge management -- Periodicals; 620.0028563
• Languages: English
• ISSNs: 1758-8715

Abstract

The design of a robust control scheme is considered a benchmark problem to address the uncertain dynamic parameters and un-modelled disturbances of the exoskeleton system. This work proposes a robust adaptive iterative learning (AIL) control (AILC) scheme for a paediatric exoskeleton system. Primarily, the mechanical description of the exoskeleton system is briefly presented along with the input parameters. Thereafter, the dynamic relation, invoking kinetic and potential energy of the system, is formulated via the Euler-Lagrange principle. The stability of the AILC scheme is ascertained using the Lyapunov analysis. The robustness is validated by incorporating the parametric uncertainties (varied mass) and un-modelled disturbances (trigonometric and random noises). Thereafter, the controller's performance is compared with classical iterative learning control (ILC) and exponential reaching law- sliding mode control (ERL-SMC) schemes. Finally, it is observed from simulation runs that the AIL controller has enough potential to track the desired gait trajectory accurately.