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LMI-based control synthesis of constrained Takagi-Sugeno fuzzy systems subject to L2 or L∞ disturbances.

, , , , , and . Neurocomputing, (2016)

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2.5D Evidential Grids for Dynamic Object Detection., , , and . FUSION, page 1-7. IEEE, (2019)Estimation-based control law for appproximating Takagi-Sugeno-based controller., , and . AQTR, page 1-6. IEEE, (2016)Periodic Takagi-Sugeno Observers for Individual Cylinder Spark Imbalance in Idle Speed Control Context., , and . ICINCO (1), page 302-309. SciTePress, (2015)A Novel Identification-Based Convex Control Scheme via Recurrent High-Order Neural Networks: An Application to the Internal Combustion Engine., , , and . Neural Process. Lett., 51 (1): 303-324 (2020)Obstacle Avoidance, Path Planning and Control for Autonomous Vehicles., , , , , , and . IV, page 529-534. IEEE, (2019)Air-fuel ratio fuzzy controller handling delay: Comparison with a PI/Smith., , , and . AQTR, page 1-6. IEEE, (2018)Replica of an Advanced Takagi-Sugeno discrete observer without matrix inversion., , and . FUZZ-IEEE, page 2206-2211. IEEE, (2016)A new air-fuel ratio model fixing the transport delay: Validation and control., , , and . CCTA, page 1904-1909. IEEE, (2017)LMI-based control synthesis of constrained Takagi-Sugeno fuzzy systems subject to L2 or L∞ disturbances., , , , , and . Neurocomputing, (2016)Avoiding Matrix Inversion in Takagi-Sugeno-Based Advanced Controllers and Observers., , and . IEEE Trans. Fuzzy Syst., 26 (1): 216-225 (2018)