Generalized bilinear Koopman realization from input-output data for multi-step prediction with metaheuristic optimization of lifting function and its application to real-world industrial system
Tokyo City University（东京都市大学）；Kyushu University（九州大学）；Hokkaido University（北海道大学）；ISUZU Advanced Engineering Center Ltd.（五十铃高级工程中心有限公司）
This paper introduces an input-output bilinear Koopman realization with an optimization algorithm of lifting functions. For nonlinear systems with inputs, Koopman-based modeling is effective because the Koopman operator enables a high-dimensional linear representation of nonlinear dynamics. However, traditional approaches face significant challenges in industrial applications. Measuring all system states is often impractical due to constraints on sensor installation. Moreover, the predictive performance of a Koopman model strongly depends on the choice of lifting functions, and their design typically requires substantial manual effort. In addition, although a linear time-invariant (LTI) Koopman model is the most commonly used model structure in the Koopman framework, such model exhibit limited predictive accuracy. To address these limitations, we propose an input-output bilinear Koopman modeling in which the design parameters of radial basis function (RBF)-based lifting functions are optimized using a global metaheuristic algorithm to improve long-term prediction performance. Consideration of the long-term prediction performance enhances the reliability of the resulting model. The proposed methodology is validated in simulations and experimental tests, with the airpath control system of a diesel engine as the plant to be modeled. This plant represents a challenging industrial application because it exhibits strong nonlinearities and coupled multi-input multi-output (MIMO) dynamics. These results demonstrate that the proposed input-output bilinear Koopman model significantly outperforms traditional linear Koopman models in predictive accuracy.
Maximum-likelihood reprojections for reliable Koopman predictors of parametric nonlinear dynamical systems
The University of Sydney（悉尼大学）；Technische Universität Chemnitz（开姆尼茨工业大学）；The Australian National University（澳大利亚国立大学）；Technische Universität Ilmenau（伊尔梅瑙工业大学）
On the Existence of Koopman Linear Embeddings for Controlled Nonlinear Systems
Contextual Robotics Institute, UC San Diego（加州大学圣地亚哥分校情境机器人研究所）；Department of Computing and Mathematical Sciences, California Institute of Technology（加州理工学院计算与数学科学系）
An overview of Koopman-based control: From error bounds to closed-loop guarantees
University of Stuttgart（斯图加特大学）；Technische Universitat Ilmenau（伊尔梅瑙工业大学）；University of California, Santa Barbara（加州大学圣塔芭芭拉分校）；Chemnitz University of Technology（开姆尼茨工业大学）
KOOPMAN-Luenberger Observer Design for Nonlinear Systems with Application to the Monitoring of a Latent Thermal Energy Storage
KTH Royal Institute of Technology（皇家理工学院）；Rubitherm Technologies GmbH（鲁比热姆技术有限公司）；AIT Austrian Institute of Technology GmbH, Center for Energy（奥地利技术研究院能源中心）
Physics-based Online Adaptive Koopman Model Predictive Attitude Control for Combined Spacecraft with Dynamic Uncertainties
Zhengzhou Advanced Research Institute, Harbin Institute of Technology（郑州先进研究院，哈尔滨工业大学）；Department of Control Science and Engineering, Harbin Institute of Technology（控制科学与工程系，哈尔滨工业大学）；School of Engineering, Newcastle University（纽卡斯尔大学工程学院）
Stability Analysis and Data-Driven State Estimation for Generalized Persidskii Systems with Time Delays: Theory and Experimental Validation on PMSM Drives
College of Engineering and Physical Sciences, Khalifa University（Khalifa大学工程与物理科学学院）
Equation-Free Digital Twins for Nonlinear Structural Dynamics
Department of Mechanical Engineering, Marine Technology and Arctic Group（机械工程系、海洋技术与北极组）；Aalto University（阿alto大学）；Independent Expert, Digital Twin Technology（独立专家，数字双技术）；Ribe, Denmark（丹麦里贝）；Institute for Risk and Reliability（风险与可靠性研究所）；Leibniz University Hannover（汉诺威莱比锡大学）
Learning and Control Beyond Linearity: Towards a Non-asymptotic Theory for Bilinear Systems
Cornell University（康奈尔大学）；Imperial College London（帝国理工学院）；University of Stuttgart（斯图加特大学）；University of Washington Seattle（华盛顿大学西雅图分校）；Amazon Inc.（亚马逊公司）
