2018/09/28 by Bowen Huang, Xu Ma, Huang, Bowen +3 · 12 citations
Decision Sciences · Engineering · Mathematics · Physics and Astronomy · #Control Systems and Identification #FOS: Mathematics #Model Reduction and Neural Networks #Optimization and Control (math.OC) #Probabilistic and Robust Engineering Design #math.OC
paper · pdf · doi:10.48550/arxiv.1810.00089
Accepted for IEEE Conference on Decision and Control(CDC) 2018
arxiv created 2018/09/28 · openalex publication_date 2018/09/28 · arxiv updated 2018/10/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we provide a systematic approach for the design of stabilizing feedback controllers for nonlinear control systems using the Koopman operator framework. The Koopman operator approach provides a linear representation for a nonlinear dynamical system and a bilinear representation for a nonlinear control system. The problem of feedback stabilization of a nonlinear control system is then transformed to the stabilization of a bilinear control system. We propose a control Lyapunov function (CLF)-based approach for the design of stabilizing feedback controllers for the bilinear system. The search for finding a CLF for the bilinear control system is formulated as a convex optimization problem. This leads to a schematic procedure for designing CLF-based stabilizing feedback controllers for the bilinear system and hence the original nonlinear system. Another advantage of the proposed controller design approach outlined in this paper is that it does not require explicit knowledge of system dynamics. In particular, the bilinear representation of a nonlinear control system in the Koopman eigenfunction space can be obtained from time-series data. Simulation results are presented to verify the main results on the design of stabilizing feedback controllers and the data-driven aspect of the proposed approach.