A Multidimensional Spline-Based Global Nonlinear Aerodynamic Model for the Cessna Citation II

Advances in Aerospace Guidance, Navigation and Control

Kampen

et al. 2015

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]

Section: Fault Tolerant Flight Control Methodsmentioning

confidence: 99%

Nonlinear and Fault-Tolerant Flight Control Using Multivariate Splines

Tol

Advances in Aerospace Guidance, Navigation and Control

Kampen

et al. 2015

“…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

Journal of Guidance, Control, and Dynamics

Kampen

et al. 2014

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.

“…This model was identified using flight data collected during 76 longitudinal 3211 flight test maneuvers. 21 Flight path reconstruction techniques based on an iterated extended Kalman filter (IEKF) were used to get a crisp estimation of aircraft state. 32 The linear regression scheme for multivariate simplex splines from Sec.…”

Section: Aerodynamic Model Validationmentioning

confidence: 99%

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

AIAA Atmospheric Flight Mechanics Conference

Mulder

2011

Self Cite

The validation of aerodynamic models created using flight test data is a time consuming and often costly process. In this paper a new method for the validation of global nonlinear aerodynamic models based on multivariate simplex splines is presented. This new method uses the unique properties of the multivariate simplex splines, a recent type of of multivariate spline, to speedup the process of aerodynamic model validation. Multivariate simplex splines are defined on non-rectangular domains and can be used to accurately fit scattered nonlinear datasets in any number of dimensions. The simplex splines consist of piecewise defined, ordinary multivariate polynomials with a predefined continuity between neighboring polynomial pieces. A recent method for nonlinear system identification based on multivariate simplex splines was used to create a global nonlinear aerodynamic model of the Cessna Citation II laboratory aircraft operated by the Delft University of Technology. In this paper, the multivariate spline based aerodynamic model for the pitching moment coefficient will be validated using both a model residual analysis as well as a statistical model quality analysis. It will be demonstrated that these new analysis methods, which are both unique to the multivariate simplex splines, provide a highly efficient and powerful new method for aerodynamic model validation.