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Showing papers by "Yuanqing Xia published in 2022"

DOI•
Active Disturbance Rejection Altitude Control for a QuadPlane

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Hailong Zhao1, Yuanqing Xia1, Dailiang Ma1, Chenxi Hao1, Feng Yu1 •
Beijing Institute of Technology1
01 Jan 2022
TL;DR: In this paper, a quadplane platform is constructed and a model-based nonlinear weight assignment principle and an active disturbance rejection control (ADRC) strategy are proposed to improve the altitude control performance during the transition phase between two flight modes.
Abstract: Quadplane is a hybrid aircraft which combines both quadrotor and traditional fixed-wing aircraft, and is also called fixed-wing vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV). Quadplane keeps the high speed and endurance of fixed-wing UAVs, and has the ability of VTOL hovering as quadrotors. In this paper, a quadplane platform is constructed. Moreover, to improve the altitude control performance during the transition phase between two flight modes, a model-based nonlinear weight assignment principle and an active disturbance rejection control (ADRC) strategy are proposed. Finally, the effectiveness of aerodynamic modelling and controller is verified via simulations.

2 citations

DOI•
Multiple–Model UKF/CKF State Estimation for Nonlinear Systems

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Xiaodi Shi1, Liping Yan1, Yuanqing Xia1, Bo Xiao2•
Beijing Institute of Technology1, Beijing University of Posts and Telecommunications2
01 Jan 2022
TL;DR: In this paper, a new UKF/CKF frame combined with a multiple model method is presented to settle a matter caused by model uncertainties, and the simulation results and comparison analysis demonstrate that the multiple-model UKF(MMUKF) and the multiple model CKF(MMCKF) have higher precision and stronger robustness than the traditional UKF and CKF in case of model uncertainties.
Abstract: In most control systems, modeling error and noise interference will always lead to the performance degradation and divergence of the UKF or the CKF. To settle a matter caused by model uncertainties, a new UKF/CKF frame combined with multiple model method is presented in this paper. Through probabilistic multiple model design method, this paper approximates the posterior densities by a finite number of probabilistically weighted points and uses these points to display the entire state space. Simulation results and comparison analysis demonstrate that the multiple-model UKF(MMUKF) and the multiple-model CKF(MMCKF) have higher precision and stronger robustness than the traditional UKF and CKF in case of model uncertainties.