Validating the Multidimensional Spline Based Global Aerodynamic Model for the Cessna Citation II

Visser, Coen C. de; Mulder, J.A.

doi:10.2514/6.2011-6356

Cited by 7 publications

(4 citation statements)

References 23 publications

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“…The aim of this setup is to update an a-priory aerodynamic model online in case of a failure with adaptive simplex splines to eliminate model errors within the NDI control system. In [12] the F-16 aerodynamic model is identified with multivariate splines using flight testing based system identification techniques from [10,16,17]. This is an accurate global model and is used as the a-priori nominal onboard aerodynamic model in this study.…”

Section: Fault Tolerant Flight Control Methodsmentioning

confidence: 99%

Nonlinear and Fault-Tolerant Flight Control Using Multivariate Splines

Tol

Visser

Kampen

et al. 2015

Advances in Aerospace Guidance, Navigation and Control

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This paper presents a study on fault tolerant flight control of a high performance aircraft using multivariate splines. The controller is implemented by making use of spline model based adaptive nonlinear dynamic inversion (NDI). This method, indicated as SANDI, combines NDI control with nonlinear control allocation based on an onboard aerodynamic spline model and a real-time identification routine. The controller is tested for an aileron hardover failure and structural damages which change the global aerodynamic properties of the aircraft. It is shown that the controller can quickly tune itself in failure conditions without the need of failure detection and monitoring algorithms. Instead, self-tuning innovation based forgetting is applied to reconfigure the onboard aerodynamic model. The controller is able to tune itself each time a model error is detected and does not require any external triggers for re-identification. Multivariate splines have a high local approximation power and are able to accurately model nonlinear aerodynamics over the entire flight envelope of an aircraft. As a result the identification routine gives a robust adaption of the aerodynamic model in case of a failure. φ , θ , ψ roll, pitch and yaw angle [rad] δ e , δ a , δ r , δ le f elevator, aileron, rudder and leading edge flap control surface deflection [rad]

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Section: Fault Tolerant Flight Control Methodsmentioning

confidence: 99%

Nonlinear and Fault-Tolerant Flight Control Using Multivariate Splines

Tol

Visser

Kampen

et al. 2015

Advances in Aerospace Guidance, Navigation and Control

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“…Recently, multivariate simplex spline, an advanced form of splines, has been introduced for global aerodynamic modelling. [15][16][17] Incidentally, necessary calculations for parameter estimation increase with the number of variables and the degree of splines.…”

Section: Literature Reviewmentioning

confidence: 99%

Nonlinear aerodynamic model identification using empirical mode decomposition

Bagherzadeh

Sabzeparvar

Karrari

2014

Proceedings of the Institution of Mechanical Engineers, Part G:

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A conceptual method based on the empirical mode decomposition algorithm is proposed to identify a high-fidelity full-flight envelope aerodynamic model, utilising flight data. The key idea is to recognise dominant phenomena of flight containing dissimilar amplitudes and frequencies by means of the empirical mode decomposition. Being distinguished and separated from each other, flight modes can be considered in the aerodynamic model, independently. To achieve the goal, an equation error method is utilised to identify six multi-input single-output systems for aerodynamic forces and moments. The inputs of the identification systems are intrinsic mode functions of flight parameters. The nonparametric identification problem uses the Hammerstein nonlinear ARMAX structure and estimates parameters by the least squares iterative algorithm. Flight tests of an unmanned aircraft in two complex manoeuvres are employed for the modelling and simulation. Several models with different nonlinear functions are introduced and trained by the first dataset. Then, to verify the identified model, the flight simulation is performed for the second dataset. Results indicate the appropriate performance of the identification method in nonlinear aerodynamics.

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“…In this section methods from [11,13,14] are used to identify the F-16 aerodynamic model using multivariate splines and ordinary polynomials. The data used to identify the aerodynamic models is generated with a high-fidelity wind tunnel dataset of the F-16.…”

Section: Identification Of the F-16 Aerodynamic Modelmentioning

confidence: 99%

“…Multivariate simplex splines have recently been used in a framework for aerodynamic model identification [12][13][14], where it was shown that they can more accurately approximate both local and global scale system nonlinearities than methods based on ordinary polynomials. The proven advantages of the simplex splines as powerful, numerically stable, and transparent nonlinear function approximators makes them well suited to replace current onboard models, thereby improving the performance and robustness of nonlinear model-based flight control systems.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Multivariate Spline-Based Control Allocation for High-Performance Aircraft

Tol

Visser

Kampen

et al. 2014

Journal of Guidance, Control, and Dynamics

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High performance flight control systems based on the nonlinear dynamic inversion (NDI) principle require highly accurate models of aircraft aerodynamics. In general, the accuracy of the internal model determines to what degree the system nonlinearities can be canceled; the more accurate the model, the better the cancellation, and with that, the higher the performance of the controller. In this paper a new control system is presented that combines NDI with multivariate simplex spline based control allocation. We present three control allocation strategies which use novel expressions for the analytical Jacobian and Hessian of the multivariate spline models. Multivariate simplex splines have a higher approximation power than ordinary polynomial models, and are capable of accurately modeling nonlinear aerodynamics over the entire flight envelope of an aircraft. This new method, indicated as SNDI, is applied to control a high performance aircraft (F-16) with a large flight envelope. The simulation results indicate that the SNDI controller can achieve feedback linearization throughout the entire flight envelope, leading to a significant increase in tracking performance compared to ordinary polynomial based NDI.

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Validating the Multidimensional Spline Based Global Aerodynamic Model for the Cessna Citation II

Cited by 7 publications

References 23 publications

Nonlinear and Fault-Tolerant Flight Control Using Multivariate Splines

Nonlinear and Fault-Tolerant Flight Control Using Multivariate Splines

Nonlinear aerodynamic model identification using empirical mode decomposition

Nonlinear Multivariate Spline-Based Control Allocation for High-Performance Aircraft

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