Actuators, cilt.11, sa.111, ss.1-26, 2022 (SCI-Expanded)
Rehabilitation is an area of robotics in which human–robot collaboration occurs, requiring
adaptation and compliance. Pneumatic artificial muscles (PAM) are soft actuators that have built-
in compliance making them usable for rehabilitation robots. Conversely, compliance arises from
nonlinear characteristics and generates obstructions in modeling and controlling actions. It is a critical
issue limiting the use of PAM. In this work, multi-input single-output (MISO) inverse modeling and
inverse dynamics model learning approaches are combined to obtain a novel nonlinear adaptive
control scheme for single PAM-actuated 1-DoF rehabilitation devices, for instance, continuous passive
motion (CPM) devices. The objective of the proposed system is to bring an alternative solution
to the compliant operation of PAM while performing exercise trajectories, to satisfy requirements
such as larger range of motion (ROM) and adaptability to external load impedance variations. The
control system combines the operation of a nonlinear autoregressive network with exogenous inputs
(NARX)-based inverse dynamics estimator used as a global range controller and cascade PIDs for
local position and pressure loops. Implementation results demonstrated the efficacy of the introduced
method in terms of compliant operation for dynamic external load variations as well as a stable
operation in case of impulsive disturbances. To summarize, a simple but efficient method is illustrated
to facilitate the common use of PAM