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Exponential uncertainty and quantization error: ℋ2 approach to networked control.

, , and . ICCA, page 985-990. IEEE, (2010)

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Semi-Global Leader-Following Output Consensus of Discrete-Time Linear Multi-Agent Systems with Input Saturation., , and . RCAR, page 354-359. IEEE, (2018)Parametrization controller design subject to finite channel rate and memoryless coding scheme., , and . ICCA, page 996-1001. IEEE, (2010)Further results on the maximal contractively invariant ellipsoid of discrete-time linear systems with multiple inputs subject to actuator saturation., and . CDC, page 6311-6316. IEEE, (2014)Intelligent Trainer for Model-Based Reinforcement Learning., , , and . CoRR, (2018)Power Series Classification: A Hybrid of LSTM and a Novel Advancing Dynamic Time Warping., , , and . CoRR, (2016)Semi-Global Leader-Following Output Consensus of Multi-Agent Systems with Input Saturation: The State Feedback Case., , and . ACC, page 2467-2472. IEEE, (2018)Design of Saturation-Based Switching Anti-Windup Gains for the Enlargement of the Domain of Attraction., and . IEEE Trans. Automat. Contr., 58 (7): 1810-1816 (2013)An Efficient Hierarchical Identity-Based Encryption Scheme for the Key Escrow., , and . SpaCCS, volume 10656 of Lecture Notes in Computer Science, page 108-120. Springer, (2017)Learning-based power prediction for data centre operations via deep neural networks., , , and . E2DC@e-Energy, page 6:1-6:10. ACM, (2016)Baconian: A Unified Opensource Framework for Model-Based Reinforcement Learning., , , and . CoRR, (2019)