Inverse Differential Riccati Equation to Optimized Human-Robot Collaboration
<|reference_start|>Inverse Differential Riccati Equation to Optimized Human-Robot Collaboration: This paper presents a framework for human-robot collaboration that integrates optimal trajectory generation with a robust tracking control strategy. The proposed framework leverages the inverse differential Riccati equation to optimize the collaboration dynamics, providing an efficient method to generate time-varying, task-specific trajectories for the human-robot system. To ensure the accurate tracking of these trajectories, a neuro-adaptive PID control method is implemented, capable of compensating for system uncertainties and variations. This control strategy dynamically adjusts the PID gains using a radial basis function neural network, ensuring both stability and adaptability. Simulations demonstrate the method's effectiveness in achieving optimized human-robot collaboration and accurate joint-space tracking, making it suitable for real-world applications.<|reference_end|>
@article{nohooji2024inverse,
    title={Inverse Differential Riccati Equation to Optimized Human-Robot
  Collaboration},
    author={Hamed Rahimi Nohooji, Holger Voos},
    journal={arXiv preprint arXiv:2409.13052},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.13052},
    primaryClass={cs.RO}
}