Fuzzy stable inversion-based output tracking for nonlinear non-minimum phase system and application to FAHVs

DOI10.1016/j.jfranklin.2015.09.001zbMath1395.93323OpenAlexW1860748303MaRDI QIDQ1660796

Lixian Zhang, Xiaoxiang Hu, Bashir Ahmad, Yang Guo, Ahmed Alsaedi, Tasawar Hayat

Publication date: 16 August 2018

Published in: Journal of the Franklin Institute (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jfranklin.2015.09.001

zbMATH Keywords

flexible air-breathing hypersonic vehiclesfuzzy stableoutput tracking controlMIMO nonlinear non-minimum phase system

Mathematics Subject Classification ID

Sensitivity (robustness) (93B35) Feedback control (93B52) Fuzzy control/observation systems (93C42) Nonlinear systems in control theory (93C10) Application models in control theory (93C95) Design techniques (robust design, computer-aided design, etc.) (93B51) Linearizations (93B18) Realizations from input-output data (93B15)

Related Items

Nonlinear adaptive tracking control of non-minimum phase hypersonic flight vehicles with unknown input nonlinearity, Unmeasurable flexible dynamics monitoring and tracking controller design for guidance and control system of hypersonic flight vehicle

Cites Work

• Adaptive restricted trajectory tracking for a non-minimum phase hypersonic vehicle model
• Nonlinear control systems.
• Stable inversion for nonlinear systems
• Tracking in a class of nonminimum-phase systems with nonlinear internal dynamics via sliding mode control using method of system center
• Adaptive sliding mode tracking control for a flexible air-breathing hypersonic vehicle
• Output tracking for nonlinear non-minimum phase systems with output delay and application to an F-16 jet fighter
• Non-linear stable inversion-based output tracking control for a spherical inverted pendulum
• Nonlinear inversion-based output tracking
• HOSM Observer for a Class of Non-Minimum Phase Causal Nonlinear MIMO Systems
• Dynamic output feedback control of a flexible air-breathing hypersonic vehicle via T–S fuzzy approach