Adaptive Compensation Control of Synthetic Jet Actuator Arrays for Airfoil Virtual Shaping

Deb, Dipankar; Tao, Gang; Burkholder, Jason O.; Smith, Douglas R.

doi:10.2514/1.24910

Cited by 42 publications

(40 citation statements)

References 9 publications

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“…By calculating the time derivative of (9) and substituting (1) and (8), the open-loop error system dynamics are obtained as…”

Section: Control Developmentmentioning

confidence: 99%

“…Figure 2 shows the virtual deflection angle versus voltage for four different values of the SJA parameter θ * 1 . A well-accepted empirically determined model of the SJA's dynamics can be expressed as [8,10,12,13] …”

Section: Dynamic Model and Propertiesmentioning

confidence: 99%

“…In addition, SJAs can expand the usable range of the AoA, improving aircraft maneuverability [7]. Arrays containing multiple SJAs can be utilized to achieve aircraft tracking control [8,9]. By using SJAs as replacements for mechanical control surfaces (e.g., elevators and ailerons), radar cross-section can be reduced, and UAV weight, cost, and mechanical complexity can also be reduced.…”

Section: Introductionmentioning

confidence: 99%

“…In addition to the challenges involved in control design in the presence of SJA actuator uncertainty, control design for UAV in off-nominal operating conditions (e.g., wind gusts) creates further challenges. Various approaches have recently been developed for aircraft tracking control using SJAs (e.g., see [8,[10][11][12][13]), where the SJA actuator uncertainty is compensated using adaptive control methods or neural networks. Other popular approaches for SJA-based control are computational fluid dynamics-(CFD-) based numerical techniques (see [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28]).…”

Section: Introductionmentioning

confidence: 99%

“…The state and input matrices, A and B, and reference state and reference input matrices, A m and B m , are defined based on the Barron Associates nonlinear tailless aircraft model (BANTAM) (for further details of the simulation model, see [8]). The 3-DOF linearized model for the BANTAM was obtained analytically during trim conditions, where M = 0 455 is the Mach number, α = 2 7 deg is angle of attack, and β s = 0 deg denotes the side slip angle.…”

mentioning

confidence: 99%

See 4 more Smart Citations

Synthetic Jet Actuator-Based Aircraft Tracking Using a Continuous Robust Nonlinear Control Strategy

Ramos-Pedroza¹,

MacKunis²,

Reyhanoglu³

2017

International Journal of Aerospace Engineering

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A robust nonlinear control law that achieves trajectory tracking control for unmanned aerial vehicles (UAVs) equipped with synthetic jet actuators (SJAs) is presented in this paper. A key challenge in the control design is that the dynamic characteristics of SJAs are nonlinear and contain parametric uncertainty. The challenge resulting from the uncertain SJA actuator parameters is mitigated via innovative algebraic manipulation in the tracking error system derivation along with a robust nonlinear control law employing constant SJA parameter estimates. A key contribution of the paper is a rigorous analysis of the range of SJA actuator parameter uncertainty within which asymptotic UAV trajectory tracking can be achieved. A rigorous stability analysis is carried out to prove semiglobal asymptotic trajectory tracking. Detailed simulation results are included to illustrate the effectiveness of the proposed control law in the presence of wind gusts and varying levels of SJA actuator parameter uncertainty.

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“…By calculating the time derivative of (9) and substituting (1) and (8), the open-loop error system dynamics are obtained as…”

Section: Control Developmentmentioning

confidence: 99%

Section: Dynamic Model and Propertiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

Synthetic Jet Actuator-Based Aircraft Tracking Using a Continuous Robust Nonlinear Control Strategy

Ramos-Pedroza¹,

MacKunis²,

Reyhanoglu³

2017

International Journal of Aerospace Engineering

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Sliding mode estimation and closed‐loop active flow control under actuator uncertainty

Kidambi

MacKunis

Drakunov

et al. 2020

Intl J Robust & Nonlinear

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This article presents a nonlinear closed-loop active flow control (AFC) method, which achieves asymptotic regulation of a fluid flow velocity field in the presence of actuator uncertainty and sensor measurement limitations. To achieve the result, a reduced-order model of the flow dynamics is derived, which utilizes proper orthogonal decomposition (POD) to express the Navier-Stokes equations as a set of nonlinear ordinary differential equations. The reduced-order model formally incorporates the actuation effects of synthetic jet actuators (SJA). Challenges inherent in the resulting POD-based reduced-order model include (1) the states are not directly measurable, (2) the measurement equation is in a nonstandard mathematical form, and (3) the SJA model contains parametric uncertainty. To address these challenges, a sliding mode observer (SMO) is designed to estimate the unmeasurable states in the reduced-order model of the actuated flow field dynamics. A salient feature of the proposed SMO is that it formally compensates for the parametric uncertainty inherent in the SJA model. The SMO is rigorously proven to achieve local finite-time estimation of the unmeasurable state in the presence of the parametric uncertainty in the SJA. The state estimates are then utilized in a nonlinear control law, which regulates the flow field velocity to a desired state. A Lyapunov-based stability analysis is provided to prove local asymptotic regulation of the flow field velocity. To illustrate the performance of the proposed estimation and AFC method, comparative numerical simulation results are provided, which demonstrate the improved performance that is achieved by incorporating the uncertainty compensator. K E Y W O R D S robust control, sliding mode estimation, uncertainty List of Symbols , flow field velocity p , flow field pressure (s) , POD mode (s) , actuation mode x(t) , time-varying Galerkin coefficient

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Adaptive Control of Single Chamber Two-Population MFC

Patel

Deb

Dey

et al. 2019

Intelligent Systems Reference Library

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Adaptive Compensation Control of Synthetic Jet Actuator Arrays for Airfoil Virtual Shaping

Cited by 42 publications

References 9 publications

Synthetic Jet Actuator-Based Aircraft Tracking Using a Continuous Robust Nonlinear Control Strategy

Synthetic Jet Actuator-Based Aircraft Tracking Using a Continuous Robust Nonlinear Control Strategy

Sliding mode estimation and closed‐loop active flow control under actuator uncertainty

Adaptive Control of Single Chamber Two-Population MFC

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