An Inverse Dynamics-Based Control Approach for Compliant Control of Pneumatic Artificial Muscles

Abstract

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.