Feedback Stabilization Using Koopman Operator
Bowen Huang, Xu Ma, Umesh Vaidya

TL;DR
This paper introduces a data-driven method for stabilizing nonlinear control systems by transforming them into bilinear systems using the Koopman operator, and designing controllers via convex optimization without explicit system models.
Contribution
It presents a novel CLF-based control design framework for bilinear systems derived from Koopman eigenfunctions, enabling stabilization without explicit dynamics knowledge.
Findings
Successful stabilization of nonlinear systems demonstrated in simulations.
Data-driven Koopman approach effectively constructs bilinear models from time-series data.
Convex optimization simplifies the search for control Lyapunov functions.
Abstract
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…
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Taxonomy
TopicsModel Reduction and Neural Networks · Probabilistic and Robust Engineering Design · Control Systems and Identification
