State Estimation and Control of an Unmanned Air Vehicle from a Ground-Based 3D Laser Scanner

2016 ◽  
Vol 28 (6) ◽  
pp. 878-886 ◽  
Author(s):  
Ryan Arya Pratama ◽  
◽  
Akihisa Ohya
Keyword(s):  
State Estimation ◽  
Position Control ◽  
Laser Scanner ◽  
Low Frequency ◽  
The State ◽  
Velocity Control ◽  
Unmanned Air Vehicle ◽  
Air Vehicle ◽  
Estimation And Control ◽  
And Control

[abstFig src='/00280006/12.jpg' width='300' text='UAV state estimation from laser scanner' ] In this work, we present a system to estimate the state of and control an Unmanned Air Vehicle (UAV) from a ground-based 3D laser scanner. The main contributions of this work are on data fusion between a low-frequency 3D laser scanner with considerable delay and an on-board 6-DOF IMU, and on automatic position control of a UAV using state estimate obtained from the fusion. We measured laser delay using data from a manually controlled flight. We have devised a method to perform online estimation and compensation of accelerometer offset using delay-corrected laser measurement. We then use the UAV state estimation in a nested controller with a high-frequency velocity control inner loop and a low-frequency position control outer loop. We demonstrated the state estimation and control in a series of experiments on velocity control and position control, including a comparison between position control using fusion data and only laser data.

EXPERIMENTAL RESULTS ON COMMAND AND CONTROL OF UNMANNED AIR VEHICLE SYSTEMS

2007 ◽  
Vol 40 (15) ◽  
pp. 239-244 ◽  
Author(s):  
Pedro Almeida ◽  
Ricardo Bencatel ◽  
Gil M. Gonçalves ◽  
JoãTo Borges Sousa ◽  
Christoph Ruetz
Keyword(s):  
Command And Control ◽  
Experimental Results ◽  
Unmanned Air Vehicle ◽  
Air Vehicle ◽  
Vehicle Systems ◽  
And Control

scholarly journals Macroscopic Traffic Flow Control Via State Estimation

2015 ◽  
Vol 15 (5) ◽  
pp. 5-16
Author(s):  
H. Abouaïssa ◽  
H. Majid
Keyword(s):  
Flow Control ◽  
State Estimation ◽  
Traffic Flow ◽  
Traffic Control ◽  
Traffic Density ◽  
Traffic State ◽  
Loop Detectors ◽  
Traffic State Estimation ◽  
Estimation And Control ◽  
And Control

Abstract The studies presented in this paper deal with traffic control in case of missing data and/or when the loop detectors are faulty. We show that the traffic state estimation plays an important role in traffic prediction and control. Two approaches are presented for the estimation of the main traffic variables (traffic density and mean speed). The state constructors obtained are then used for traffic flow control. Several numerical simulations show very promising results for both traffic state estimation and control.

Computational development of a practical educational tool for state estimation of power systems using the MATLAB optimization toolbox

2018 ◽  
Vol 56 (2) ◽  
pp. 105-123 ◽  
Author(s):  
EA Zamora-Cárdenas ◽  
A Pizano-Martínez ◽  
JM Lozano-García ◽  
VJ Gutiérrez-Martínez ◽  
R Cisneros-Magaña
Keyword(s):  
Power Systems ◽  
State Estimation ◽  
Electric Power ◽  
Smart Grids ◽  
Weighted Least Squares ◽  
Electric Power Systems ◽  
The State ◽  
Estimation Problem ◽  
Educational Tool ◽  
And Control

State estimation is one of the most important processes to perform a reliable monitoring and control of the steady-state operating condition of modern electric power systems; thus, it is currently a fundamental part in the development of research to enhance the monitoring and security of the smart grids operation. This important topic is taught in advanced courses of operation and control of power systems, for graduate and undergraduate power engineering students. However, the most used software packages for simulation and analysis of power systems by researchers, students, and educators have put little attention on the state estimation module. Due to this fact, this paper proposes an approach to develop the computational implementation of a practical educational tool for state estimation of electric power systems using the MATLAB optimization toolbox. In this proposal, the formulation of the state estimation problem consists of developing a general digital code to implement an objective function based on the weighted least squares method. While the lsqnonlin function of the MATLAB optimization toolbox solves the formulated state estimation problem. Simplifying both research and educational processes, this tool helps graduate and undergraduate students to improve learning, understanding, and the times of implementation and development of research in state estimation. Simulations of an equivalent model of the Mexican interconnected power system consisting of 190 buses and 46 machines are used to test and validate the proposal performance.

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