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• W2735630565 abstract "Model order reduction for nonlinear systems has been a challenging problem with limited methods and tools available. One approach is balanced truncation of empirically obtained Gramians (Lall et al., 1999) or controllability/ observability covariance matrices (Hahn et al., 2002). Empirical Gramians provide information about the controllability/ observability which is then used to transform a system so that only the subspaces that are most important to the input-to-output behavior are retained. While this provides an open-loop reduced order model, it may not be the best choice for robust controller and estimator (compensator) design. In this paper, an approach for obtaining empirical control and filter Riccati covariance matrices is proposed, and the empirical Riccati covariance matrices are used to obtain a reduced order model for systematic closed-loop compensator design. The proposed reduction method is demonstrated on a spatially discretized catalytic rod model, for which a constrained nonlinear model predictive controller and an extended Kalman filter are designed with the reduced order model. The efficacy of the proposed approach is established by comparing input and output responses of the resulting closed-loop system to those of the full order compensator and reduced compensators designed using other common balanced truncations methods." @default.
• W2735630565 created "2017-07-21" @default.
• W2735630565 creator A5002488026 @default.
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• W2735630565 date "2017-05-01" @default.
• W2735630565 modified "2023-09-25" @default.
• W2735630565 title "Empirical Riccati covariance matrices for closed-loop model order reduction of nonlinear systems by balanced truncation" @default.
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• W2735630565 doi "https://doi.org/10.23919/acc.2017.7963484" @default.
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