Linear Model Predictive Control for Quadrotors with An Analytically Derived Koopman Model
<|reference_start|>Linear Model Predictive Control for Quadrotors with An Analytically Derived Koopman Model: This letter presents a Koopman-theoretic lifted linear parameter-varying (LPV) system with countably infinite dimensions to model the nonlinear dynamics of a quadrotor on SE(3) for facilitating control design. The LPV system evolves in time in the space of the observables, called the lifted space. A primary challenge in utilizing the Koopman-based linearization is identifying a set of observables that can adequately span the lifted space, with the majority of the current methods using data to learn these observables. In this study, we analytically derive the observables for the quadrotor dynamics on SE(3) to formulate the lifted LPV system. The lifted LPV system has a countably infinite dimension which is then truncated for practical control design. The truncation is analytically justified by showing vanishing residual property in a bounded trajectory regime. The LPV system is then approximated as a linear time-invariant (LTI) system with a set of virtual control inputs. The controllability of the lifted LTI system is translatable to the true quadrotor system on SE(3). A linear model-predictive control (LMPC) scheme is formulated and implemented in numerical simulations employing this LTI framework for various tracking problems, with attention given to the potential for real-time implementation.<|reference_end|>
@article{rajkumar2024linear,
    title={Linear Model Predictive Control for Quadrotors with An Analytically
  Derived Koopman Model},
    author={Santosh M. Rajkumar, Sheng Cheng, Naira Hovakimyan, Debdipta Goswami},
    journal={arXiv preprint arXiv:2409.12374},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.12374},
    primaryClass={eess.SY cs.SY}
}