Koopman Linearization for Data-Driven Batch State Estimation of Control-Affine Systems
نویسندگان
چکیده
We present the Koopman State Estimator (KoopSE), a framework for model-free batch state estimation of control-affine systems that makes no linearization assumptions, requires problem-specific feature selections, and has an inference computational cost is independent number training points. lift original nonlinear system into higher-dimensional Reproducing Kernel Hilbert Space (RKHS), where becomes bilinear. The time-invariant model matrices can be learned by solving least-squares problem on trajectories. At test time, algebraically manipulated linear time-varying system, standard techniques used to efficiently compute means covariances. Random Fourier Features (RFF) are combine efficiency Koopman-based methods generality kernel-embedding methods. KoopSE validated experimentally localization task involving mobile robot equipped with ultra-wideband receivers wheel odometry. estimates more accurate consistent than model-based extended Rauch-Tung-Striebel (RTS) smoother, despite having prior knowledge system’s motion or measurement models.
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ژورنال
عنوان ژورنال: IEEE robotics and automation letters
سال: 2022
ISSN: ['2377-3766']
DOI: https://doi.org/10.1109/lra.2021.3133587