TY - JOUR
T1 - Data-Driven Passivity-Based Control of Underactuated Mechanical Systems via Interconnection and Damping Assignment
AU - Sirichotiyakul, Wankun
AU - Satici, Aykut C.
N1 - Publisher Copyright:
© 2022 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2023/6
Y1 - 2023/6
N2 - Since its introduction in the late 1980s, passivity-based control (PBC) has proven to be successful in controlling many robotic systems. The connection between stability and passivity theory is the most attractive feature of controllers designed using this methodology. However, the need to solve nonlinear partial differential equations (PDE) in closed-form has been a major challenge in applying PBC to general robotic systems. Here, we introduce a systematic approach to design controllers for a class of underactuated mechanical systems based on interconnection and damping assignment. Exploiting the universal approximation capability of neural networks, we formulate a data-driven optimisation problem that discovers solutions to the required PDEs automatically. Our approach does not destroy the passivity structure, preserving the inherent stability properties. We demonstrate the efficacy of our framework on two benchmark problems: the inertia wheel pendulum and the ball and beam system.
AB - Since its introduction in the late 1980s, passivity-based control (PBC) has proven to be successful in controlling many robotic systems. The connection between stability and passivity theory is the most attractive feature of controllers designed using this methodology. However, the need to solve nonlinear partial differential equations (PDE) in closed-form has been a major challenge in applying PBC to general robotic systems. Here, we introduce a systematic approach to design controllers for a class of underactuated mechanical systems based on interconnection and damping assignment. Exploiting the universal approximation capability of neural networks, we formulate a data-driven optimisation problem that discovers solutions to the required PDEs automatically. Our approach does not destroy the passivity structure, preserving the inherent stability properties. We demonstrate the efficacy of our framework on two benchmark problems: the inertia wheel pendulum and the ball and beam system.
KW - machine learning
KW - neural network
KW - nonlinear control
KW - optimisation
KW - robotics
KW - underactuated
UR - http://www.scopus.com/inward/record.url?scp=85127148485&partnerID=8YFLogxK
UR - https://scholarworks.boisestate.edu/mecheng_facpubs/208
U2 - 10.1080/00207179.2022.2051750
DO - 10.1080/00207179.2022.2051750
M3 - Article
SN - 0020-7179
VL - 96
SP - 1448
EP - 1456
JO - International Journal of Control
JF - International Journal of Control
IS - 6
ER -