Koopman operator learning for predictive control via Khatri-Rao kernel regression
For researchers in nonlinear control, this provides a data-driven approach to learn predictive models with reduced prediction error, though validation is limited to a single chaotic system.
This paper develops a data-driven Koopman operator learning method using Khatri-Rao kernel regression for predictive control, achieving stabilization of an unstable equilibrium in the chaotic Lorenz system from a range of initial conditions.
This paper develops a data-driven realization of the generalized Koopman operator (GeKo), in which states and inputs are lifted independently and the dynamics are expressed as a tensor bilinear system. The first contribution is a time-sequenced multi-step Khatri-Rao kernel regression formulation that exposes the operator to evolved snapshots along trajectories rather than only single one-step pairs, which reduces compounded prediction error. Secondly, we develop a kernel- and input-agnostic structured SVD reduction that compresses the lifted state and input spaces while preserving the Khatri-Rao realization. We instantiate the framework with random Fourier features and describe a complete predictive-control pipeline, including a multi-step roll-out diagnostic that guides the choice of MPC horizon. The framework is validated on the chaotic Lorenz system, where the learned reduced-order GeKo model stabilizes an unstable equilibrium from a range of initial conditions.