A Koopman Set-Membership Approach for Nonlinear Data-Driven Control with Stability Guarantees
It provides a novel data-driven control method with stability guarantees for unknown nonlinear systems, addressing a key challenge in nonlinear control.
This paper proposes a data-driven controller design for unknown nonlinear systems using a Koopman bilinear realization with set-membership identification, ensuring stability via a rational state-feedback controller solved by sum-of-squares programming. Numerical examples demonstrate effectiveness.
This paper proposes a data-driven controller design method for unknown nonlinear systems based on a Koopman bilinear realization. Using Koopman operator theory, the nonlinear system can be represented as a bilinear discrete-time system with a residual error term. The residual error is proportionally bounded by the norm of the lifted state and input, while the system matrices of the bilinear model are unknown. Assuming that bounds on the residual error are available, the unknown system matrices are characterized via a set-membership representation using the collected input-state data pairs of the nonlinear system. A data-driven controller design method is proposed to ensure stability for all bilinear systems within this set-membership description and for all admissible residual errors. More specifically, we design a rational state-feedback controller that stabilizes the bilinear model with residual error and, consequently, the original nonlinear system, by solving a sum-of-squares (SOS) program. The effectiveness of the proposed approach is demonstrated through numerical examples.