Search

Avoiding high computational loads is essential to online aerodynamic model identi- fication 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 square errors (LMMSE) estimation. The new method is compared to existing batch and recursive MVSB methods in a numerical experiment in which an aerodynamic model is recursively identified based on data from an NASA F-16 wind-tunnel model., Control & Operations, Aerospace Engineering

Avoiding high computational loads is essential to online aerodynamic model identi- fication 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 square errors (LMMSE) estimation. The new method is compared to existing batch and recursive MVSB methods in a numerical experiment in which an aerodynamic model is recursively identified based on data from an NASA F-16 wind-tunnel model., Control & Operations, Aerospace Engineering

This paper presents a flight control strategy based on nonlinear dynamic inversion. The approach presented, called incremental nonlinear dynamic inversion, uses properties of general mechanical systems and nonlinear dynamic inversion by feeding back angular accelerations. Theoretically, feedback of angular accelerations eliminates sensitivity to model mismatch, greatly increasing the robust performance of the system compared with conventional nonlinear dynamic inversion. However, angular accelerations are not readily available. Furthermore, it is shown that angular acceleration feedback is sensitive to sensor measurement time delays. Therefore, a linear predictive filter is proposed that predicts the angular accelerations, solving the time delay and angular acceleration availability problem. The predictive filter uses only references and measurements of angular rates. Hence, the proposed control method makes incremental nonlinear dynamic inversion practically available using conventional inertial measurement units., Control and Simulation Division, Aerospace Engineering

This paper presents a flight control strategy based on nonlinear dynamic inversion. The approach presented, called incremental nonlinear dynamic inversion, uses properties of general mechanical systems and nonlinear dynamic inversion by feeding back angular accelerations. Theoretically, feedback of angular accelerations eliminates sensitivity to model mismatch, greatly increasing the robust performance of the system compared with conventional nonlinear dynamic inversion. However, angular accelerations are not readily available. Furthermore, it is shown that angular acceleration feedback is sensitive to sensor measurement time delays. Therefore, a linear predictive filter is proposed that predicts the angular accelerations, solving the time delay and angular acceleration availability problem. The predictive filter uses only references and measurements of angular rates. Hence, the proposed control method makes incremental nonlinear dynamic inversion practically available using conventional inertial measurement units., Control and Simulation Division, Aerospace Engineering