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Controlling soft continuum manipulator arms is difficult due to theirinfinite degrees of freedom, nonlinear material properties, and largedeflections under loading. This paper presents a data-driven approach toidentifying soft manipulator models that enables consistent control undervariable loading conditions. This is achieved by incorporating loads into alinear Koopman operator model as states and estimating their values online viaan observer within the control loop. Using this approach, real-time, fullyautonomous control of a pneumatically actuated soft continuum manipulator isachieved. In several trajectory following experiments, this controller is shownto be more accurate and precise than controllers based on models that areunable to explicitly account for loading. The manipulator also successfullyperforms pick and place of objects with unknown mass, demonstrating theefficacy of this approach in executing real-world manipulation tasks.

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