Topic
System identification
About: System identification is a research topic. Over the lifetime, 21291 publications have been published within this topic receiving 439142 citations.
Papers published on a yearly basis
Papers
More filters
TL;DR: In this paper, the identification of nonlinear aeroelastic systems based on the Volterra theory of non-linear systems is presented, and the application of higher-order spectra (HOS) to wind-tunnel flutter data is discussed.
Abstract: The identification of nonlinear aeroelastic systems based on the Volterra theory of nonlinear systems is presented. Recent applications of the theory to problems in computational and experimental aeroelasticity are reviewed. Computational results include the development of computationally efficient reduced-order models (ROMs) using an Euler/Navier–Stokes flow solver and the analytical derivation of Volterra kernels for a nonlinear aeroelastic system. Experimental results include the identification of aerodynamic impulse responses, the application of higher-order spectra (HOS) to wind-tunnel flutter data, and the identification of nonlinear aeroelastic phenomena from flight flutter test data of the active aeroelastic wing (AAW) aircraft.
207 citations
TL;DR: In this article, the authors compare the performance of three recursive parameter estimation algorithms for aerodynamic parameter estimation of two aircraft from real flight data, including Extended Kalman Filter (EKF), unscented Kalman filter (UKF), and augmented version of the UKF.
206 citations
TL;DR: In this paper, the Steiglitz-McBride algorithm, the eigensystem realization algorithm, and the Prony method were used to identify low order linear systems of power systems modeled in standard transient stability programs.
Abstract: This paper describes the results of a study to evaluate the performance of three identification methods for the study of low frequency electromechanical oscillations. The three identification methods considered are: the Steiglitz-McBride algorithm; the eigensystem realization algorithm; and the Prony method. The identification methods are used to identify low order linear systems of power systems modeled in standard transient stability programs. This is accomplished by processing the system response to a simple probing pulse. The frequency domain characteristics of several identified systems are compared using three power systems with lightly damped electromechanical modes.
206 citations
01 Jan 2017
TL;DR: By the end of this chapter, the reader should be able to implement an MPC to achieve trajectory tracking for both multi-rotor systems and fixed-wing UAVs.
Abstract: In this chapter, strategies for Model Predictive Control (MPC) design and implementation for Unmaned Aerial Vehicles (UAVs) are discussed. This chapter is divided into two main sections. In the first section, modelling, controller design and implementation of MPC for multi-rotor systems is presented. In the second section, we show modelling and controller design techniques for fixed-wing UAVs. System identification techniques are used to derive an estimate of the system model, while state of the art solvers are employed to solve the optimization problem online. By the end of this chapter, the reader should be able to implement an MPC to achieve trajectory tracking for both multi-rotor systems and fixed-wing UAVs.
206 citations
TL;DR: Methods for model validation of continuous-time nonlinear systems with uncertain parameters are presented, which employ functions of state-parameter-time whose existence proves that a model and a feasible parameter set are inconsistent with some time-domain experimental data.
205 citations