Koopman-Based Control of a Soft Continuum Manipulator Under Variable Loading Conditions

Abstract

Controlling soft continuum manipulator arms is difficult due to their infinite degrees of freedom, nonlinear material properties, and large deflections under loading. This letter presents a data-driven approach to identifying soft manipulator models that enables consistent control under variable loading conditions. This is accomplished by incorporating loads into a linear Koopman operator model as states and estimating their values online via an observer within the control loop. Using this approach, autonomous control of a pneumatically actuated soft continuum manipulator is achieved. In several trajectory following experiments, this controller is shown to be more accurate and precise than controllers based on models that do not explicitly account for loading. The manipulator also successfully performs pick and place of objects with unknown mass, demonstrating the efficacy of this approach in executing real-world manipulation tasks.