Aug 25, 2022 · We seek to combine the nonlinear modeling capabilities of a wide class of neural networks with the constraint-handling guarantees of model ...

We seek to combine the nonlinear modeling capabilities of a wide class of neural networks with the constraint-handling guarantees of model predictive control ( ...

This paper proposes Koopman operator-based Stochastic Model Predictive Control (K-SMPC) for enhanced lateral control of autonomous vehicles.

Jun 2, 2022 · This paper presents robust Koopman model predictive control (RK-MPC), a framework that leverages the training errors of data-driven models to ...

We use tools from the recently proposed Koopman operator framework to identify a quasi-linear parameter-varying model (in input/output and state-space form) by ...

Abstract: A fast data-driven extension of the velocity-based quasi-linear parameter-varying model predictive control (qLMPC) approach is proposed for scenarios ...

We propose a tracking error-based learning model based on the Koopman operator theory. We collect the data with an existing controller in the loop and ...

Oct 31, 2023 · The basis functions can be constructed using time-delayed coordinates of the outputs, enabling the application to purely data-driven systems.

In this paper, we propose a novel data-driven approach for learning and control of quadrotor UAVs based on the Koopman operator and extended dynamic mode ...

In this paper, we focus on incorporating model-based data-driven control using the Koopman operator theory. The first contribution we proposed in this paper ...