Koopman Meets Discrete-Time Control Barrier Functions: A Linear Model Predictive Control Framework
This work addresses safety-critical control for robotics and autonomous systems, offering a computationally efficient solution that is incremental in combining existing methods.
The paper tackles the computational challenge of safety-critical control for nonlinear discrete-time systems by proposing a Koopman-based linear model predictive control framework that transforms nonlinear safety constraints into a quadratic program, enabling safe trajectory generation and efficient real-time control in simulations.
This paper proposes a Koopman-based linear model predictive control (LMPC) framework for safety-critical control of nonlinear discrete-time systems. Existing MPC formulations based on discrete-time control barrier functions (DCBFs) enforce safety through barrier constraints but typically result in computationally demanding nonlinear programming. To address this challenge, we construct a DCBF-augmented dynamical system and employ Koopman operator theory to lift the nonlinear dynamics into a higher-dimensional space where both the system dynamics and the barrier function admit a linear predictor representation. This enables the transformation of the nonlinear safety-constrained MPC problem into a quadratic program (QP). To improve feasibility while preserving safety, a relaxation mechanism with slack variables is introduced for the barrier constraints. The resulting approach combines the modeling capability of Koopman operators with the computational efficiency of QP. Numerical simulations on a navigation task for a robot with nonlinear dynamics demonstrate that the proposed framework achieves safe trajectory generation and efficient real-time control.