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Rauh A., Senkel L. (Eds.) Variable-Structure Approaches. Analysis, Simulation, Robust Control and Estimation of Uncertain Dynamic Processes

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Rauh A., Senkel L. (Eds.) Variable-Structure Approaches. Analysis, Simulation, Robust Control and Estimation of Uncertain Dynamic Processes
Springer International Publishing, 2016. — 361 p. — (Mathematical Engineering) — ISBN: 978-3-319-31539-3 (eBook), 978-3-319-31537-9 (Hardcover).
This edited book aims at presenting current research activities in the field of robust variable-structure systems. The scope equally comprises highlighting novel methodological aspects as well as presenting the use of variable-structure techniques in industrial applications including their efficient implementation on hardware for real-time control. The target audience primarily comprises research experts in the field of control theory and nonlinear dynamics but the book may also be beneficial for graduate students.
Comparison of Backstepping-Based Sliding Mode and Adaptive Backstepping for a Robust Control of a Twin Rotor Helicopter
Robust Congestion Controller for a Single Virtual Circuit in Connection-Oriented Communication Networks
Interval Methods for Robust Sliding Mode Control Synthesis of High-Temperature Fuel Cells with State and Input Constraints
Experimental and Numerical Validation of a Reliable Sliding Mode Control Strategy Considering Uncertainty with Interval Arithmetic
A Sliding Mode Control with a Bang–Bang Observer for Detection of Particle Pollution
Sliding Mode Control for a Hydrostatic Transmission in Combination with a Sliding Mode Observer
Sliding Mode Observation with Iterative Parameter Adaption for Fast-Switching Solenoid Valves
Sliding Mode Observer for Fault Diagnosis: LPV and Takagi–Sugeno Model Approaches
Sliding Mode State and Fault Estimation for Decentralized Systems
Fault Diagnosis of Nonlinear Differential-Algebraic Systems Using Hybrid Estimation
Towards Robust Fault-Tolerant Model Predictive Control with Constraints for Takagi–Sugeno Systems
Constrained Model Predictive Control of Processes with Uncertain Structure Modeled by Jump Markov Linear Systems
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