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A modular adaptive control design with ISS analysis for nonminimum phase hypersonic vehicle models
Author(s) -
Mannava A.,
Serrani A.
Publication year - 2018
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.2869
Subject(s) - control theory (sociology) , setpoint , elevator , airspeed , hypersonic flight , parametric statistics , hypersonic speed , modular design , adaptive control , flight envelope , cruise control , engineering , aerodynamics , thrust , vehicle dynamics , computer science , control engineering , aerospace engineering , mathematics , control (management) , statistics , artificial intelligence , operating system
Summary Air‐breathing hypersonic vehicles typically exhibit a nonminimum phase behavior when altitude is controlled via lift generation. This phenomenon prohibits the use of classical inversion‐based control techniques. Dynamic models of these vehicles are also subject to parametric uncertainties and unmodeled dynamics related to flexible effects of the fuselage. In this paper, we present a modular adaptive control method that achieves asymptotic setpoint tracking in both airspeed and altitude using thrust and elevator deflection as the only control inputs for a generic longitudinal model of a hypersonic cruise. The nonminimum phase problem is overcome through output redefinition, with altitude controlled by pitching moment. The internal dynamics, flight path angle, and altitude are then stabilized by saturating the interconnections and exploiting local stability properties. A new technique for the use of saturation functions in error coordinate is presented. The adaptive controller for altitude uses a pitch rate observer combined with projection. This control augmentation decouples the parameter estimation errors from internal dynamics, allowing for the use of small‐gain arguments. Simulation results from a vehicle model with flexible effects and parametric uncertainty are included to demonstrate control effectiveness.