Deep Reinforcement Learning Control for Disturbance Rejection in a Nonlinear Dynamic System with Parametric Uncertainty
<|reference_start|>Deep Reinforcement Learning Control for Disturbance Rejection in a Nonlinear Dynamic System with Parametric Uncertainty: This work describes a technique for active rejection of multiple independent and time-correlated stochastic disturbances for a nonlinear flexible inverted pendulum with cart system with uncertain model parameters. The control law is determined through deep reinforcement learning, specifically with a continuous actor-critic variant of deep Q-learning known as Deep Deterministic Policy Gradient, while the disturbance magnitudes evolve via independent stochastic processes. Simulation results are then compared with those from a classical control system.<|reference_end|>
@article{hill2024deep,
    title={Deep Reinforcement Learning Control for Disturbance Rejection in a
  Nonlinear Dynamic System with Parametric Uncertainty},
    author={Vincent W. Hill},
    journal={arXiv preprint arXiv:2404.04699},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.04699},
    primaryClass={eess.SY cs.SY}
}