Online Aerodynamic Model Identification Using a Recursive Sequential Method for Multivariate Splines

Abstract

Avoiding high computational loads is essential to online aerodynamic model identification algorithms, which are at the heart of any model-based adaptive flight control system. Multivariate simplex B-spline (MVSB) methods are excellent function approximation tools for modeling the nonlinear aerodynamics of high performance aircraft.However, the computational efficiency of the MVSB method must be improved in order to enable real-time onboard applications, for example in adaptive nonlinear flight control systems. In this paper, a new recursive sequential identification strategy is proposed for the MVSB method aimed at increasing its computational efficiency, thereby allowing its use in onboard system identification applications. The main contribution of this new method is a significant reduction of computational load for large scale online identification problems as compared to the existing MVSB methods. The proposed method consists of two sequential steps for each time interval, and makes use of a decomposition of the global problem domain into a number of subdomains, called modules. In the first step the B-coefficients for each module are estimated using a least squares estimator. In the second step the local B-coefficients for each module are then smoothened into a single global B-coefficient vector using a linear minimum mean

Flight Control Using Physical Dynamic Inversion

“…Generally speaking, NDI can not be used alone, controller must be used to compensate the model error. Three approaches might be used to reduce the model error sensitivity associated with NDI: 1) Approximate the model error online and cancel it further by adaptive augmentation, Neural Networks (NN) can be used as a universal approximater and trained in real time with tracking error excitation [11][12][13][14][15][16][17][18][19] , .input dynamics like the common actuator saturation, which may cause misadaptation, can be considered and hidden by Pseudo Control Hedging (PCH), the NDI based adaptive NN controller plus PCH architecture has been successfully flight tested in a number of applications 17,18 , new development under this framework includes efficient online model substitution 20 and nonparametric adaptive nonlinearity representation 19 2) Robust mu controller augmentation. Uncertainties in model, sensor and actuator dynamics and considered in the mu framework, a robust controller is designed such that the closed loop stability retains with bounded perturbation 21,22 .…”

Command Filtered Model-Free Robust Control for Aircrafts With Actuator Dynamics

“…To handle the uncertainties in the control effectiveness matrix, techniques involving tuning functions [18], the least-squares method [19], and immersion and invariance [20] have been applied to the estimator design, and these estimators were evaluated in [21]. To improve the computational efficiency of the multivariate simplex B-spline method [22], [23], a novel recursive sequential method [24] enabling real-time onboard applications was proposed for modelling the nonlinear aerodynamics of high-performance aircraft. In [25], a novel real-time identification strategy for multivariate splines was proposed to address the aerodynamic uncertainties in the control allocation system, and the computational complexity of the novel multivariate splines was even lower than that of the recursive B-splines method developed in [24].…”