Estimation and rejection of sinusoidal disturbance with unknown frequency using cascade disturbance observer

2021 ◽  
pp. 014233122110027
Author(s):  
Wen Xinyu ◽  
Zhang Junjie ◽  
Yao Xiuming
Keyword(s):  
Disturbance Rejection ◽  
Disturbance Observer ◽  
Uncertain Systems ◽  
Estimation Method ◽  
Uncertain System ◽  
Frequency Factor ◽  
Disturbance Estimation ◽  
Stability Performance ◽  
Sinusoidal Disturbance ◽  
Order State

A disturbance rejection approach is presented for a class of uncertain systems subject to sinusoidal disturbance with unknown frequency. The proposed disturbance estimation method includes two types of observers that are connected in cascade form. The disturbance property is excited through an auxiliary filter, and then a frequency factor observer is constructed to generate the information required by full-order state observer. Thus, with the disturbance and system state estimation values, the composite control structure including a cascade disturbance observer (CDO) and a robust feedback controller is designed. As a result, the uncertain system with unknown-frequency sinusoidal component can be controlled within the disturbance observer-based control (DOBC) framework, where the asymptotic stability performance can be guaranteed.

scholarly journals Intelligent Observer-Based Feedback Linearization for Autonomous Quadrotor Control

2018 ◽  
Vol 7 (4.35) ◽  
pp. 904 ◽  
Author(s):  
Izzuddin M. Lazim ◽  
Abdul Rashid Husain ◽  
Nurul Adilla Mohd Subha ◽  
Mohd Ariffanan Mohd Basri
Keyword(s):  
Disturbance Rejection ◽  
Feedback Linearization ◽  
Disturbance Observer ◽  
Estimation Error ◽  
Activation Function ◽  
Theoretical Development ◽  
Disturbance Estimation ◽  
Hybrid Controller ◽  
Adaptation Law ◽  
Hidden Neurons

The presence of disturbances can cause instability to the quadrotor flight and can be dangerous especially when operating near obstacles or other aerial vehicles. In this paper, a hybrid controller called state feedback with intelligent disturbance observer-based control (SF-iDOBC) is developed for trajectory tracking of quadrotor in the presence of time-varying disturbances, e.g. wind. This is achieved by integrating artificial intelligence (AI) technique with disturbance observer-based feedback linearization to achieve a better disturbance rejection capability. Here, the observer estimates the disturbances acting on the quadrotor, while AI technique using the radial basis function neural network (RBFNN) compensates the disturbance estimation error. To improve the error compensation of RBFNN, the k-means clustering method is used to find the optimal centers of the Gaussian activation function. In addition, the weights of the RBFNN are tuned online using the derived adaptation law based on the Lyapunov method, which eliminates the offline training. In the simulation experiment conducted, a total of four input nodes and five hidden neurons are used to compensate for the error. The results obtained demonstrate the effectiveness and merits of the theoretical development. 

scholarly journals Unscented Kalman filter for airship model uncertainties and wind disturbance estimation

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0257849
Author(s):  
Muhammad Wasim ◽  
Ahsan Ali ◽  
Mohammad Ahmad Choudhry ◽  
Faisal Saleem ◽  
Inam Ul Hasan Shaikh ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Precision Agriculture ◽  
Model Uncertainty ◽  
Unscented Kalman Filter ◽  
Estimation Method ◽  
Wind Disturbance ◽  
Model Uncertainties ◽  
Nonlinear Simulation ◽  
Lumped Model ◽  
Disturbance Estimation

An airship is lighter than an air vehicle with enormous potential in applications such as communication, aerial inspection, border surveillance, and precision agriculture. An airship model is made up of dynamic, aerodynamic, aerostatic, and propulsive forces. However, the computation of aerodynamic forces remained a challenge. In addition to aerodynamic model deficiencies, airship mass matrix suffers from parameter variations. Moreover, due to the lighter-than-air nature, it is also susceptible to wind disturbances. These modeling issues are the key challenges in developing an efficient autonomous flight controller for an airship. This article proposes a unified estimation method for airship states, model uncertainties, and wind disturbance estimation using Unscented Kalman Filter (UKF). The proposed method is based on a lumped model uncertainty vector that unifies model uncertainties and wind disturbances in a single vector. The airship model is extended by incorporating six auxiliary state variables into the lumped model uncertainty vector. The performance of the proposed methodology is evaluated using a nonlinear simulation model of a custom-developed UETT airship and is validated by conducting a kind of error analysis. For comparative studies, EKF estimator is also developed. The results show the performance superiority of the proposed estimator over EKF; however, the proposed estimator is a bit expensive on computational grounds. However, as per the requirements of the current application, the proposed estimator can be a preferred choice.

Research on Terminal Piecewise Sliding Mode for Excitation Control System Based on the Nonlinear Disturbance Observer

2014 ◽  
Vol 711 ◽  
pp. 297-302
Author(s):  
Xiao Feng Liu ◽  
Hong Zhang ◽  
He Qiao
Keyword(s):  
Disturbance Observer ◽  
Convergence Rates ◽  
Sliding Mode ◽  
Terminal Sliding Mode ◽  
Nonlinear Disturbance Observer ◽  
Disturbance Estimation ◽  
Guarantee System ◽  
Control Precision ◽  
Nonlinear Disturbance ◽  
High Control

Aiming at uncertainty and disturbance of the excitation system, we designed a kind of precise sliding mode controller with nonlinear disturbance observer that realized disturbance estimation and compensation. In order to obtain better convergence rates, based on the basis of fast terminal sliding mode surface, terminal piecewise sliding mode is adopted. Using the single machine infinite system, we have carried on the static and transient simulation. Simulation results show that control strategy adopted can guarantee system robustness to disturbance and has high control precision

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