Nonlinear Adaptive Control of an Aeroelastic Two-Dimensional Lifting Surface

Behal, Aman; Marzocca, Piergiovanni; Rao, V.M.; Gnann, A.

doi:10.2514/1.14011

Cited by 70 publications

(48 citation statements)

References 27 publications

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“…and the control gains and are selected to satisfy the following sufficient conditions: (42) where denotes the minimum eigenvalue of the argument, is introduced in (23), is introduced in (45), , , , , and are introduced in (19), (36), and (38), and is introduced in (17). A detailed derivation of the gain conditions in (42) can be found in the Appendix.…”

Section: Theoremmentioning

confidence: 99%

“…After using (30) and (31), the time derivative of can be expressed as (33) where (34) (35) After utilizing Property 1, (24), and (26), the following inequalities can be developed: (36) where are known positive bounding constants. Based on (29), the closed-loop error system can be expressed as (37) where (38) Based on (19), (32), and (38), the following inequalities can be developed: (39) where are known positive bounding constants, and , , and are introduced in (19) and (36).…”

Section: Closed-loop Error Systemmentioning

confidence: 99%

“…Preliminary results indicate that this assumption is mild in the sense that the decomposition in (16) results in a diagonally dominant for a wide range of . Based on (16), the error dynamics in (15) are (18) where Since is positive definite, the following inequalities can be developed: (19) where are positive bounding constants. The error dynamics in (18) can now be rewritten as (20) where is a strictly upper triangular matrix, is an identity matrix, denotes a measurable regression matrix, and is a vector containing the unknown elements of the and matrices, defined via the parametrization (21) Based on the open-loop error dynamics in (20), the auxiliary control term is designed as (22) and the auxiliary control term is designed as (23) where is a constant, positive control gain, is a constant, positive definite, diagonal control gain matrix, and is introduced in (6).…”

Section: Closed-loop Error Systemmentioning

confidence: 99%

“…It is assumed in [18] that the sign of a control input coefficient is known along with the lower bound of its absolute value. An output feedback controller is presented in [19], which achieves flutter suppression and limit cycle oscillations in a nonlinear 2-D wing-flap system. Parametric uncertainty in the dynamic model is addressed using a Lyapunov-based adaptive law, and the unmeasurable states are compensated using state estimators.…”

mentioning

confidence: 99%

“…Parametric uncertainty in the dynamic model is addressed using a Lyapunov-based adaptive law, and the unmeasurable states are compensated using state estimators. The controller in [19] is shown to regulate the pitch angle to a constant set point based on the assumption that the structures of the aeroelastic model and pitch spring nonlinearity are known. While the aforementioned ADI results compensate for parameteric uncertainty, they cannot be used to yield asymptotic tracking in the presence of unmodeled additive disturbances.…”

mentioning

confidence: 99%

See 4 more Smart Citations

Global Adaptive Output Feedback Tracking Control of an Unmanned Aerial Vehicle

MacKunis

Wilcox

Kaiser

et al. 2010

IEEE Trans. Contr. Syst. Technol.

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Abstract-An output feedback (OFB) dynamic inversion control strategy is developed for an unmanned aerial vehicle (UAV) that achieves global asymptotic tracking of a reference model. The UAV is modeled as an uncertain linear time-invariant (LTI) system with an additive bounded nonvanishing nonlinear disturbance. A continuous tracking controller is designed to mitigate the nonlinear disturbance and inversion error, and an adaptive law is utilized to compensate for the parametric uncertainty. Global asymptotic tracking of the measurable output states is proven via a Lyapunovlike stability analysis, and high-fidelity simulation results are provided to illustrate the applicability and performance of the developed control law.Index Terms-Adaptive control, dynamic inversion (DI), Lyapunov methods, nonlinear control, robust control.

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Section: Theoremmentioning

confidence: 99%

Section: Closed-loop Error Systemmentioning

confidence: 99%

Section: Closed-loop Error Systemmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Global Adaptive Output Feedback Tracking Control of an Unmanned Aerial Vehicle

MacKunis

Wilcox

Kaiser

et al. 2010

IEEE Trans. Contr. Syst. Technol.

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Event‐triggered model reference adaptive control system design for SISO plants using meta‐learning‐based physics‐informed neural networks without labeled data and transfer learning

Duanyai,

Song,

Konghuayrob

et al. 2024

Adaptive Control & Signal

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SummaryThis paper examines the controllability of a novel Lyapunov‐based model reference adaptive control (MRAC) system designed with a meta‐learning‐based physics‐informed neural network (MLPINN) for linear and nonlinear single‐input and single‐output (SISO) plants without labeled data (MLPINN‐MRAC system). It is devised with the benefits of several techniques: the integration of the identification process in a training mode into an online control mode (straightforward design); no labeled data generation for online identification by a physics‐informed neural network; the prevention of degradation in tracking performance by meta‐learning, as the system triggers a meta‐learning process only when an error threshold detects the deterioration (high efficiency and the reduction of computation cost); quick adaptation to new inputs and an updated control input for each sub‐time span by transfer learning. It is worth noting that the frequency of the meta‐learning event detection significantly affects the step response stability of the nonlinear plant. To achieve a better quality of the stabilization, more frequent event detection is necessary for both beginning and end intervals in control. Sixteen triggering events are enough to shape the acceptable step response of the nonlinear plant, and 44 triggering events achieve the plant's desired step response with its minor modeling error. While, as for the linear plant, a single triggering event is sufficient to attain its tolerable step response and modeling error, implying that intensive event detection is not critical in the identification. It is obvious that the MLPINN‐MRAC system functions well and is more beneficial and efficient for the nonlinear plant.

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Aeroelastic analysis and flutter control of wings and panels: A review

Chai

Gao

Ankay

et al. 2021

Int Journal of Mech Sys Dyn

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Flutter is a self-excited vibration under the interaction of the inertial force, aerodynamic force, and elastic force of the structure. After the flutter occurs, the aircraft structures will exhibit limit cycle oscillation, which will cause catastrophic accidents or fatigue damage to the structures. Therefore, it is of great theoretical and practical significance to study the aeroelastic characteristics and flutter control for improving the aeroelastic stability of aircraft structures. This paper reviews the recent advances in aeroelastic analysis and flutter control of wings and panel structures. The mechanism of aeroelastic flutter of wings and panels is presented. The research methods of aeroelastic flutter for different structures developed in recent years are briefly summarized. Various control strategies including the linear and nonlinear control algorithms as well as the active flutter control results of wings and panels are presented. Finally, the paper ends with conclusions, which highlight challenges of the development in aeroelastic analysis and flutter control, and provide a brief outlook on the future investigations. This study aims to present a comprehensive understanding of aeroelastic analysis and flutter control. It can also provide guidance on the design of new wings and panel structures for improving their aeroelastic stability.

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Nonlinear Adaptive Control of an Aeroelastic Two-Dimensional Lifting Surface

Cited by 70 publications

References 27 publications

Global Adaptive Output Feedback Tracking Control of an Unmanned Aerial Vehicle

Global Adaptive Output Feedback Tracking Control of an Unmanned Aerial Vehicle

Event‐triggered model reference adaptive control system design for SISO plants using meta‐learning‐based physics‐informed neural networks without labeled data and transfer learning

Aeroelastic analysis and flutter control of wings and panels: A review

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