SIAM DS21: Michael Banks - Koopman Mode Analysis and Control of a Soft Robotic Arm скачать в хорошем качестве

SIAM DS21: Michael Banks - Koopman Mode Analysis and Control of a Soft Robotic Arm

Abstract. Due to their inherent flexibility and versatility, soft robots open up the field of robotics to a new range of capabilities not available to rigid robots. Due to their essentially infinite dimensional, nonlinear dynamics, the modeling and control of these systems from first principles is not practical. Instead, data-driven methods are preferred. We apply the data-driven approximation of the Koopman operator to the modeling and control of a pneumatic soft robot arm. The resulting Koopman mode decomposition is used to distinguish the robot's fundamental dynamic modes and classify them based on frequency and decay rate. Existing research uses a black-box approach, whereas we use the decomposition of the dynamics into Koopman modes to reveal the underlying physics without knowledge of any governing equations. This approach also allows us to amplify or stabilize specific behaviors in order to achieve control objectives. Authors Michael Banks, University of California, Santa Barbara, U.S., mjbanks@ucsb.edu David Haggerty, University of California, Santa Barbara, U.S., davidhaggerty@ucsb.edu Patrick Curtis, University of California, Santa Barbara, U.S., patrick_curtis@ucsb.edu Igor Mezic, University of California, Santa Barbara, U.S., mezic@ucsb.edu Elliot Hawkes, University of California, Santa Barbara, U.S., ewhawkes@ucsb.edu