Optimal timing of the observation for the state estimation and control of the stochastic discrete linear system

1978 ◽  
Vol 27 (4) ◽  
pp. 621-637 ◽  
Author(s):  
YOSHIKAZU SAWARAGI ◽  
TAKASHI SOEDA ◽  
YUTAKA TOMITA ◽  
ISAO IMAI
Keyword(s):  
Linear System ◽  
State Estimation ◽  
The State ◽  
Optimal Timing ◽  
Estimation And Control ◽  
And Control

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.

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.

scholarly journals Nonlinear State Estimation and Control for Chaos Suppression in MEMS Resonator

2013 ◽  
Vol 20 (4) ◽  
pp. 749-761 ◽  
Author(s):  
Angelo Marcelo Tusset ◽  
Átila Madureira Bueno ◽  
Claudinor Bitencourt Nascimento ◽  
Mauricio dos Santos Kaster ◽  
José Manoel Balthazar
Keyword(s):  
State Estimation ◽  
Multiple Scales ◽  
Chaotic Behavior ◽  
Control Strategies ◽  
The State ◽  
Linear Feedback ◽  
Chaos Suppression ◽  
State Dependent ◽  
Mems Resonators ◽  
Estimation And Control

During the last decade the chaotic behavior in MEMS resonators have been reported in a number of works. Here, the chaotic behavior of a micro-mechanical resonator is suppressed. The aim is to control the system forcing it to an orbit of the analytical solution obtained by the multiple scales method. The State Dependent Riccati Equation (SDRE) and the Optimal Linear Feedback Control (OLFC) strategies are used for controlling the trajectory of the system. Additionally, the SDRE technique is used in the state estimator design. The state estimation and the control techniques proved to be effective in controlling the trajectory of the system. Additionally, the robustness of the control strategies are tested considering parametric errors and measurement noise in the control loop.

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