Nonlinear Control of Robotic Manipulators Driven by Pneumatic Artificial Muscles

Abstract

Lightweight, compliant actuators are particularly desirable in safety-conscious robotic systems intended for interaction with humans. Pneumatic artificial muscles (PAMs) exhibit these characteristics and are capable of higher specific work than comparably sized hydraulic actuators and electric motors. However, control of PAM-actuated systems has proven difficult due to the highly nonlinear nature of the actuators and the pneumatic systems driving their actuation. This study develops and investigates the performance of three advanced control strategies—sliding mode control, adaptive sliding mode control, and adaptive neural network (ANN) control—each containing a distinct level of a priori model knowledge, to enable smooth and accurate motion tracking of a single degree-of-freedom PAM-actuated manipulator. Originally developed by J.-J. Slotine and R.M. Sanner, the specific controllers employed in this study are significantly modified for application to pneumatically actuated open-chain manipulators with complex nonlinear dynamics. The two adaptive controllers are updated online and require no pretraining step. Several experiments are performed with each controller to evaluate and compare closed-loop tracking performance. Results highlight the dependence of a preferred control strategy on the level of model completeness and quality, and suggest that in most PAM-actuated manipulator scenarios, the ANN controller is preferable because it does not require a model of the pneumatic system or joint mechanism design, which can be difficult and time consuming to characterize, and is robust to changes in PAM actuator characteristics (due to fatigue or replacement).