Tesi etd-01082026-110946 |
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Tipo di tesi
Tesi di laurea magistrale
Autore
MORELLI, VITTORIO
URN
etd-01082026-110946
Titolo
Partial-State Feedback Model Reference Adaptive Control Applied to Non-Minimum Phase Aircraft Autopilots
Dipartimento
INGEGNERIA CIVILE E INDUSTRIALE
Corso di studi
INGEGNERIA AEROSPAZIALE
Relatori
relatore Prof. Di Rito, Gianpietro
Parole chiave
- adaptive control
- control systems
- controllability
- detectability
- e-modification
- flight dynamics
- model reference adaptive control
- mrac
- non-minimum phase systems
- observability
- partial-state feedback control
- stabilizability
Data inizio appello
16/02/2026
Consultabilità
Non consultabile
Data di rilascio
16/02/2096
Riassunto
This thesis, developed in collaboration with MBDA Italia S.p.A., investigates the appli
cation of Partial-State Feedback Model Reference Adaptive Control (MRAC) to an aircraft
acceleration autopilot with the objective of increasing its robustness without drastically sac
rificing its performance. “Adaptive Control” refers to a category of control system that have
the ability to adjust control design parameters such as control gains online based on inputs
received by the plant in order to accommodate system uncertainty. In MRAC the controller
parameters are adapted in such a way that the system behavior matches a reference model
whose performances are defined a priori and are representative of the desired behavior. The
choice of a Partial-State Feedback MRAC, as opposed to a conceptually simpler Full-State
Feedback MRAC, is due to the fact that in most practical applications not all the states are
available for measurement. The control strategy studied in this thesis will be applied to an
aircraft controlled by a pre-existing optimized autopilot in order to enhance vertical and lat
eral acceleration tracking at the same time. The system is neither observable nor controllable,
conditions generally deemed necessary for MRAC usage, but only detectable and stabilizable.
The system is also non-minimum phase, a characteristic that can cause stability issues. For this
reason, a robustness modification has been introduced and its stability has been proven through
the Lyapunov stability theorem. A series of Monte Carlo analyses, carried out in multiple flight conditions, demonstrate the MRAC effectiveness in reducing the error with respect to the nominal
behavior in the presence of parametric uncertainties and disturbances that arise from the lateral-longitudinal flight dynamics coupling. However, stability issues occur when the plant to be
controlled has poles with low damping.
cation of Partial-State Feedback Model Reference Adaptive Control (MRAC) to an aircraft
acceleration autopilot with the objective of increasing its robustness without drastically sac
rificing its performance. “Adaptive Control” refers to a category of control system that have
the ability to adjust control design parameters such as control gains online based on inputs
received by the plant in order to accommodate system uncertainty. In MRAC the controller
parameters are adapted in such a way that the system behavior matches a reference model
whose performances are defined a priori and are representative of the desired behavior. The
choice of a Partial-State Feedback MRAC, as opposed to a conceptually simpler Full-State
Feedback MRAC, is due to the fact that in most practical applications not all the states are
available for measurement. The control strategy studied in this thesis will be applied to an
aircraft controlled by a pre-existing optimized autopilot in order to enhance vertical and lat
eral acceleration tracking at the same time. The system is neither observable nor controllable,
conditions generally deemed necessary for MRAC usage, but only detectable and stabilizable.
The system is also non-minimum phase, a characteristic that can cause stability issues. For this
reason, a robustness modification has been introduced and its stability has been proven through
the Lyapunov stability theorem. A series of Monte Carlo analyses, carried out in multiple flight conditions, demonstrate the MRAC effectiveness in reducing the error with respect to the nominal
behavior in the presence of parametric uncertainties and disturbances that arise from the lateral-longitudinal flight dynamics coupling. However, stability issues occur when the plant to be
controlled has poles with low damping.
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