An L1 Adaptive Pitch Controller for Miniature Air Vehicles

Beard, Randal W.; Knoebel, Nathan; Cao, Chengyu; Hovakimyan, Naira; Matthews, Joshua S.

doi:10.2514/6.2006-6777

Cited by 27 publications

(20 citation statements)

References 15 publications

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“…Unlike conventional adaptive controllers, the L 1 adaptive controllers adapt fast, leading to desired transient and asymptotic tracking with a guaranteed, bounded away from zero, time-delay margin [27]. The results from [5,6] have been intensively applied in flight tests [11,[28][29][30][31] and various mid-to high-fidelity simulation environments [32][33][34][35]. Insights into the performance of L 1 adaptive controller can be obtained from the analysis of a simple linear system in [36], in which sensitivity and cosensitivity transfer functions are analyzed for disturbance rejection and noise tolerance in the presence of a large adaptation rate.…”

Section: Introductionmentioning

confidence: 98%

L1 Adaptive Output-Feedback Controller for Non-Strictly-Positive-Real Reference Systems: Missile Longitudinal Autopilot Design

Cao

Hovakimyan

2009

Journal of Guidance, Control, and Dynamics

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113

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This paper presents an extension of the L 1 adaptive output-feedback controller to systems of unknown relative degree in the presence of time-varying uncertainties without restricting the rate of their variation. As compared with earlier results in this direction, a new piecewise continuous adaptive law is introduced, along with the low-passfiltered control signal that allows for achieving arbitrarily close tracking of the input and the output signals of the reference system, the transfer function of which is not required to be strictly positive real. Stability of this reference system is proved using a small-gain-type argument. The performance bounds between the closed-loop reference system and the closed-loop L 1 adaptive system can be rendered arbitrarily small by reducing the step size of integration. Missile longitudinal autopilot design is used as an example to illustrate the theoretical results.

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Section: Introductionmentioning

confidence: 98%

L1 Adaptive Output-Feedback Controller for Non-Strictly-Positive-Real Reference Systems: Missile Longitudinal Autopilot Design

Cao

Hovakimyan

2009

Journal of Guidance, Control, and Dynamics

Self Cite

113

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“…Flight control has long been a privileged applicative field for robust and gain-scheduling control, see, eg, previous works [1][2][3][4][5][6] and more recently, for fault-tolerant and adaptive control. [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22] Noting that strong links exist between all these fields. Indeed, robust and adaptive control can be seen as competing/complementary techniques for solving the same problem of controlling an uncertain plant.…”

Section: Introductionmentioning

confidence: 99%

“…24 Nevertheless, a physical aerodynamic state-space model is available for an aircraft (A/C), and state-space methods are typically used to design robust or gain-scheduled flight control laws so that adaptive flight controllers are usually designed using state-space methods, see, eg, previous works. 8,[10][11][12][13][14][15][16][18][19][20][21][22] Generally speaking, 2 main problems need to be solved in adaptive control, namely, to obtain a priori guaranteed stability and performance properties of the adaptive closed loop and to decrease the online computational time and complexity. In the context of indirect adaptive control, following the works of Ferreres and Antoinette, 25,26 a solution based on robust and gain-scheduled control tools is to design off-line a gain-scheduled controller, depending on the plant parameters to be estimated, to avoid the complexity of implementing a control design algorithm online.…”

Section: Introductionmentioning

confidence: 99%

Adaptive LFT control of a civil aircraft with online frequency‐domain parameter estimation

Ferreres

Hardier²

2017

Intl J Robust & Nonlinear

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Summary This paper describes the application of an indirect linear fractional transformation (LFT)–based state‐space adaptive control scheme to a transport aircraft, within the context of the European project REconfiguration of CONtrol in Flight for Integral Global Upset REcovery. The principle of the scheme is to design and validate off‐line a gain‐scheduled controller, depending on the plant parameters to be estimated, and to combine it online with a model estimator, so as to minimize the onboard computational time and complexity. A modal approach, very classical for the design of a flight control law, is used to directly synthesize the static output feedback LFT controller, depending on the control and stability derivatives, ie, the parameters of the linearized aerodynamic state‐space model to be estimated. Since the gain‐scheduled LFT controller online depends on the parameter estimates instead of the true values, its robustness to transient and asymptotic estimation errors needs to be assessed using μ and integral quadratic constraint analysis techniques. A primary concern being an online implementation, a fully recursive frequency‐domain estimation technique is proposed, with a low online computational burden and the capability to track time‐varying parameters. Full nonlinear simulations along a trajectory validate the good performance properties of the combined estimator and gain‐scheduled flight controller. To some extent, minimal guaranteed stability and performance properties of the adaptive scheme can be ensured by switching to a robust controller when the parameter estimates are not reliable enough, thus bypassing the Certainty Equivalence Principle.

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“…The MRAC controller was tested for the uncertainty in aerodynamic coefficients by deploying the flap during flight tests. Beard et al [7] used a L1 adaptive algorithm to control the pitch attitude loop for MAVs. Simulation and flight test showed that L1 adaptive controller exhibited robustness for variable sample rates, as well as for time delays.…”

Section: Introductionmentioning

confidence: 99%

Turbulence Effects on Modified State Observer-Based Adaptive Control: Black Kite Micro Aerial Vehicle

2016

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This paper presents the implementation of a modified state observer-based adaptive dynamic inverse controller for the Black Kite micro aerial vehicle. The pitch and velocity adaptations are computed by the modified state observer in the presence of turbulence to simulate atmospheric conditions. This state observer uses the estimation error to generate the adaptations and, hence, is more robust than model reference adaptive controllers which use modeling or tracking error. In prior work, a traditional proportional-integral-derivative control law was tested in simulation for its adaptive capability in the longitudinal dynamics of the Black Kite micro aerial vehicle. This controller tracks the altitude and velocity commands during normal conditions, but fails in the presence of both parameter uncertainties and system failures. The modified state observer-based adaptations, along with the proportional-integral-derivative controller enables tracking despite these conditions. To simulate flight of the micro aerial vehicle with turbulence, a Dryden turbulence model is included. The turbulence levels used are based on the absolute load factor experienced by the aircraft. The length scale was set to 2.0 meters with a turbulence intensity of 5.0 m/s that generates a moderate turbulence. Simulation results for various flight conditions show that the modified state observer-based adaptations were able to adapt to the uncertainties and the controller tracks the commanded altitude and velocity. The summary of results for all of the simulated test cases and the response plots of various states for typical flight cases are presented.

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An L1 Adaptive Pitch Controller for Miniature Air Vehicles

Cited by 27 publications

References 15 publications

L1 Adaptive Output-Feedback Controller for Non-Strictly-Positive-Real Reference Systems: Missile Longitudinal Autopilot Design

L1 Adaptive Output-Feedback Controller for Non-Strictly-Positive-Real Reference Systems: Missile Longitudinal Autopilot Design

Adaptive LFT control of a civil aircraft with online frequency‐domain parameter estimation

Turbulence Effects on Modified State Observer-Based Adaptive Control: Black Kite Micro Aerial Vehicle

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