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Reinforcement Learning Policies With Local LQR Guarantees For Nonlinear Discrete-Time Systems.

, , , , , and . CDC, page 2258-2263. IEEE, (2021)

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Optimal observer design for disturbed state affine systems., , and . ACC, page 2733-2738. IEEE, (2018)Closed loop optimal experiment design for on-line parameter estimation., , , and . ECC, page 1813-1818. IEEE, (2014)Data-driven multi-model control for a waste heat recovery system., , , , and . CDC, page 5501-5506. IEEE, (2020)Feedback control and estimation of crystal size distribution in a cooling batch crystallizer based on reachability analysis., , and . CDC, page 6382-6387. IEEE, (2012)Learning Reduced Nonlinear State-Space Models: an Output-Error Based Canonical Approach., , , , and . CDC, page 150-155. IEEE, (2022)Improving the control performance of an Organic Rankine Cycle system for waste heat recovery from a heavy-duty diesel engine using a model-based approach., , , , , , and . CDC, page 6830-6836. IEEE, (2013)Constant gain observer for continuous-discrete time uniformly observable systems., , and . CDC, page 5406-5411. IEEE, (2006)Deep Learning-based Luenberger observer design for discrete-time nonlinear systems., and . CDC, page 4370-4375. IEEE, (2021)Reinforcement Learning Policies With Local LQR Guarantees For Nonlinear Discrete-Time Systems., , , , , and . CDC, page 2258-2263. IEEE, (2021)EAGLE: Large-scale Learning of Turbulent Fluid Dynamics with Mesh Transformers., , , , , and . ICLR, OpenReview.net, (2023)