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Robotic Mirror Therapy System for Functional Recovery of Hemiplegic Arms
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Reinforcement learning for port-hamiltonian systems.

Olivier Sprangers, Robert Babuška, Subramanya P Nageshrao

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    |August 29, 2014
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    This study introduces reinforcement learning (RL) to energy-balancing passivity-based control (EB-PBC) for port-Hamiltonian systems. The method enhances control design by incorporating performance criteria and enabling stability assessment through energy shaping.

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    Area of Science:

    • Control Theory
    • Robotics
    • Machine Learning

    Background:

    • Passivity-based control (PBC) offers intuitive stabilization for port-Hamiltonian systems.
    • Traditional PBC often lacks performance considerations and requires solving complex partial differential equations (PDEs).

    Purpose of the Study:

    • To integrate reinforcement learning (RL) into energy-balancing passivity-based control (EB-PBC).
    • To develop a parameterized EB-PBC method that includes performance criteria and robustness.
    • To enable the search for near-optimal control policies for port-Hamiltonian systems.

    Main Methods:

    • Parameterization of EB-PBC preserving PDE matching conditions.
    • Actor-Critic (AC) reinforcement learning to find control law parameters.
    • Application to the pendulum swing-up problem in simulations and experiments.

    Main Results:

    • Learned control policies are interpretable in terms of energy shaping and damping injection.
    • Stability assessment is possible using passivity theory.
    • The approach integrates port-Hamiltonian systems into the AC framework, accelerating learning.

    Conclusions:

    • The proposed RL-based EB-PBC method effectively addresses limitations of traditional PBC.
    • It enables performance-driven control design and robust stability analysis for port-Hamiltonian systems.
    • The method demonstrates practical applicability through successful pendulum swing-up demonstrations.