Reconfigurable Control Allocation Technology Using Weighted Least Squares for Nonlinear System in Unmanned Aerial Vehicle

2010 ◽  
Author(s):  
Qing-Li Zhou ◽  
Youmin Zhang ◽  
Camille-Alain Rabbath ◽  
Didier Theilliol
Keyword(s):  
Nonlinear System ◽  
Least Squares ◽  
Unmanned Aerial Vehicle ◽  
Weighted Least Squares ◽  
Control Allocation ◽  
Reconfigurable Control ◽  
Aerial Vehicle

scholarly journals Control and flight test of a tilt-rotor unmanned aerial vehicle

2017 ◽  
Vol 14 (1) ◽  
pp. 172988141667814 ◽  
Author(s):  
Chao Chen ◽  
Jiyang Zhang ◽  
Daibing Zhang ◽  
Lincheng Shen
Keyword(s):  
Unmanned Aerial Vehicle ◽  
High Speed ◽  
Cross Coupling ◽  
Flight Test ◽  
Control Allocation ◽  
Tilt Rotor ◽  
Satisfactory Performance ◽  
Allocation Method ◽  
Aerial Vehicle ◽  
The Cost

Tilt-rotor unmanned aerial vehicles have attracted increasing attention due to their ability to perform vertical take-off and landing and their high-speed cruising abilities, thereby presenting broad application prospects. Considering portability and applications in tasks characterized by constrained or small scope areas, this article presents a compact tricopter configuration tilt-rotor unmanned aerial vehicle with full modes of flight from the rotor mode to the fixed-wing mode and vice versa. The unique multiple modes make the tilt-rotor unmanned aerial vehicle a multi-input multi-output, non-affine, multi-channel cross coupling, and nonlinear system. Considering these characteristics, a control allocation method is designed to make the controller adaptive to the full modes of flight. To reduce the cost, the accurate dynamic model of the tilt-rotor unmanned aerial vehicle is not obtained, so a full-mode flight strategy is designed in view of this situation. An autonomous flight test was conducted, and the results indicate the satisfactory performance of the control allocation method and flight strategy.

Fault estimation for a quadrotor unmanned aerial vehicle by integrating the parity space approach with recursive least squares

2017 ◽  
Vol 232 (4) ◽  
pp. 783-796 ◽  
Author(s):  
Weixin Han ◽  
Zhenhua Wang ◽  
Yi Shen
Keyword(s):  
Fault Detection ◽  
Least Squares ◽  
Unmanned Aerial Vehicle ◽  
Recursive Least Squares ◽  
Detection And Estimation ◽  
Recursive Least Squares Algorithm ◽  
Aerial Vehicle ◽  
Parity Space ◽  
Least Squares Algorithm ◽  
Space Approach

This paper studies actuator fault detection and estimation for a quadrotor unmanned aerial vehicle. By combining a parity space approach and a recursive least squares algorithm, we propose a novel fault detection and estimation method strategy for a quadrotor unmanned aerial vehicle, which is described by a linear time-varying system. Specifically, the parity space approach is used to generate a residual in fault detection, and then the magnitude of the fault is estimated by a recursive least squares algorithm with a variable forgetting factor based on the parity. Numerical simulations of a quadrotor unmanned aerial vehicle are conducted to verify the effectiveness of the proposed method.

scholarly journals Fault tolerant control of uav with wing layout based on control allocation

2021 ◽  
Vol 233 ◽  
pp. 04008
Author(s):  
Chen Jie ◽  
LIN Jianxin
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Flight Control ◽  
Fault Tolerant ◽  
Fault Tolerant Control ◽  
Control Allocation ◽  
Longitudinal Motion ◽  
Actuator Failure ◽  
Simulink Simulation ◽  
Control Command ◽  
Aerial Vehicle

As the flying wing layout unmanned aerial vehicle (uav) extensive research and task environment increasingly complex, Yu Feiyi layout unmanned aerial vehicle (uav) for fault tolerant control gradually become the main technical means of the flight control, using the established mathematical model of the flying wing uav longitudinal layout setting the actuator failure effect, is in the nature of adaptive control allocation fault-tolerant algorithm is given, and MATLAB/simulink simulation is carried out for uav longitudinal motion, realize the rapid and stable, the control command and response to complete the nonlinear fault-tolerant control of flying wing uavs.

Methodology improvements for three-dimensional UAV-based travel-time acoustic atmospheric tomography

2021 ◽  
Author(s):  
Kevin J Rogers ◽  
Anthony Finn
Keyword(s):  
Travel Time ◽  
Wind Velocity ◽  
Unmanned Aerial Vehicle ◽  
Tikhonov Regularization ◽  
Weighted Least Squares ◽  
Three Dimensional ◽  
Velocity Fields ◽  
Three Dimensions ◽  
First Case ◽  
Aerial Vehicle

AbstractThis paper describes a method for measuring continuous, three-dimensional temperature and wind velocity patterns in the Atmospheric Surface Layer (ASL) using Unmanned Aerial Vehicle-Based Acoustic Atmospheric Tomography (UBAAT). An Unmanned Aerial Vehicle (UAV) is flown over an array of microphones on the ground. The travel-time for sound rays between the UAV and each microphone is used to reconstruct 3D temperature and wind velocity fields, with the continuous motion of the UAV generating far more ray paths over much greater volumes of atmosphere than can be obtained using static speakers and microphones. Significant improvements over previous UBAAT techniques include the use of a synthetic tone rather than the natural sound generated by the UAV, use of vertical temperature and wind profiles to improve modelling of sharp changes near the ground, normalization of observations to incorporate weighted least squares techniques within Tikhonov regularization, and normalization of the model matrix to reduce bias in estimating modelling parameters when using Tikhonov regularization. This is the first case where UBAAT has been performed in three dimensions and also compared with independent temperature and wind velocity measurements. A summary of the results of simulation studies and trials results is provided, which shows that UBAAT can estimate three-dimensional temperature and wind velocity fields in the ASL with useful accuracy (approximately 1°C for temperature and 1 m/s for wind speed).

scholarly journals Fuzzy Weighted Least Squares Support Vector Regression with Data Reduction for Nonlinear System Modeling

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Xiaoyong Liu ◽  
Aijia Ouyang ◽  
Zhonghua Yun
Keyword(s):  
Nonlinear System ◽  
Least Squares ◽  
Support Vector Regression ◽  
Data Reduction ◽  
System Modeling ◽  
Weighted Least Squares ◽  
Computational Time ◽  
Support Vector ◽  
Nonlinear System Modeling ◽  
Overlap Factor

This paper proposes a fuzzy weighted least squares support vector regression (FW-LSSVR) with data reduction for nonlinear system modeling based only on the measured data. The proposed method combines the advantages of data reduction with some ideas of fuzzy weighted mechanism. It not only possesses the capability of illuminating local characteristic of the modeled plant but also can deal with the problem of boundary effects resulted from local LSSVR method when the modeled data is at the boundary of whole data subset. Furthermore, in comparison of the SVR, the proposed method only utilizes fewer hyperparameters to construct model, and the overlap factor λ can be chosen in relatively smaller value than SVR to further reduce more computational time. First of all, distilling the original input space into several regions with fuzzy partition by applying Gustafson-Kessel clustering algorithm (GKCA) is a foundation for data reduction and the overlap factor is introduced to reduce the size of subsets. Following that, those subset regression models (SRMs) which can be simultaneously solved by LSSVR are integrated into an overall output of the estimated nonlinear system by fuzzy weighted. Finally, the proposed method is demonstrated by experimental analysis and compared with local LSSVR, weighted SVR, and global LSSVR methods by using the index of computational time and root-mean-square error (RMSE).

scholarly journals An improved magnetometer calibration and compensation method based on Levenberg–Marquardt algorithm for multi-rotor unmanned aerial vehicle

2020 ◽  
Vol 53 (3-4) ◽  
pp. 276-286
Author(s):  
Helong Wu ◽  
Xinbiao Pei ◽  
Jihui Li ◽  
Huibin Gao ◽  
Yue Bai
Keyword(s):  
Least Squares ◽  
Unmanned Aerial Vehicle ◽  
Optimal Estimation ◽  
Calibration Model ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Magnetometer Data ◽  
Aerial Vehicle ◽  
Magnetometer Calibration ◽  
Yaw Angle

In order to improvethe yaw angle accuracy of multi-rotor unmanned aerial vehicle and meet the requirement of autonomous flight, a new calibration and compensation method for magnetometer based on Levenberg–Marquardt algorithm is proposed in this paper. A novel mathematical calibration model with clear physical meaning is established. “Hard iron” error and “Soft iron” error of magnetometer which affect the yaw accuracy of unmanned aerial vehicle are compensated. Initially, Levenberg–Marquardt algorithm is applied to the process of sphere fitting for the original magnetometer data; the optimal estimation of sphere radius and initial “Hard iron” error are obtained. Then, the ellipsoid fitting is performed, and the optimal estimation of “Hard iron” error and “Soft iron” error are obtained. Finally, the calibration parameters are used to compensate for the magnetometer’s output during unmanned aerial vehicle flight. Traditional ellipsoid fitting based on least squares algorithm is taken as reference to prove the effectiveness of the proposed algorithm. Semi-physical simulation experiment proves that the proposed magnetometer calibration method significantly enhances the accuracy of magnetometer. Static test shows that the yaw angle error is reduced from 1.2° to 0.4° when using the proposed calibration model to calibrate magnetometers. In dynamic tests, the sensor MTi’s output is used as reference. The data fusion of magnetometer compensated by the proposed new calibration model based on Levenberg–Marquardt algorithm can accurately track the desired attitude angle. Experimental results indicate that the accuracy of magnetometer in the yaw angle estimation has been greatly enhanced. In the process of attitude estimated, the compensation magnetometer data given by this new method have faster convergence speed, higher accuracy, and better performance than the compensation magnetometer data given by traditional ellipsoid fitting based on least squares algorithm.

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