Time delay remaining in the displacement detection of the optically trapped particles using Kalman filter

2021 ◽  
Author(s):  
Xiaofei Zeng ◽  
Bin Zhang ◽  
Xiang Han ◽  
Zhijie Chen ◽  
Wei Xiong ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Time Delay ◽  
Trapped Particles ◽  
Optically Trapped

Unscented Kalman Filter for INS/GNSS Data Fusion with Time Delay

2021 ◽  
Author(s):  
Kanishke Gamagedara ◽  
Taeyoung Lee ◽  
Murray R. Snyder
Keyword(s):  
Kalman Filter ◽  
Time Delay ◽  
Data Fusion ◽  
Unscented Kalman Filter ◽  
Gnss Data

scholarly journals Experience Rating of ARIMA Processes by the Kalman Filter

1986 ◽  
Vol 16 (1) ◽  
pp. 19-31 ◽  
Author(s):  
Jukka Rantala
Keyword(s):  
Kalman Filter ◽  
Time Delay ◽  
State Space ◽  
Theoretical Approach ◽  
Space Form ◽  
The State ◽  
Experience Rating ◽  
Filter Technique ◽  
Main Emphasis ◽  
Arima Processes

AbstractThis paper deals with experience rating of claims processes of ARIMA structures. By experience rating we mean that future premiums should be only a function of past values of the claims process. The main emphasis is on demonstrating the usefulness of the control-theoretical approach in the search for optimal rating rules. Optimality is here defined to mean as smooth a flow of premiums as possible when the variation in the accumulated profit is restricted to a certain amount. First it is shown how the underlying model in its simplest form can be transformed into the state-space form. Then the Kalman filter technique is used to find the optimal rules. Also a time delay in information is taken into account. The optimal rules are illustrated by examples.

Displacement Detection of Optically Trapped Microsphere in Vacuum Based on Kalman Filter

2020 ◽  
Vol 49 (5) ◽  
pp. 512004
Author(s):  
蒋建斌 JIANG Jian-bin ◽  
胡慧珠 HU Hui-zhu ◽  
李楠 LI Nan ◽  
陈杏藩 CHEN Xin-fan ◽  
舒晓武 SHU Xiao-wu ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Optically Trapped

scholarly journals Optimized back-focal-plane interferometry directly measures forces of optically trapped particles

2012 ◽  
Vol 20 (11) ◽  
pp. 12270 ◽  
Author(s):  
Arnau Farré ◽  
Ferran Marsà ◽  
Mario Montes-Usategui
Keyword(s):  
Focal Plane ◽  
Trapped Particles ◽  
Optically Trapped

Two Degree-of-Freedom µ Synthesis Control With Kalman Filter for Flight Environment Simulation Volume With Sensors Uncertainty

2019 ◽  
Author(s):  
Meiyin Zhu ◽  
Xi Wang ◽  
Shubo Yang ◽  
Huairong Chen ◽  
Keqiang Miao ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Time Delay ◽  
Pid Controller ◽  
Control Precision ◽  
Noise Rejection ◽  
Environment Simulation ◽  
Temperature And Pressure ◽  
Servo Tracking ◽  
Two Degree Of Freedom ◽  
Simulation Volume

Abstract Flight Environment Simulation Volume (FESV) is the most important subsystem of Altitude Ground Test Facilities (AGTF). Its control precision of temperature and pressure determines the level of test ability of AGTF. However, in practice, the sensor hysteresis and noise may greatly affect the control precision of FESV. To improve the control performance of FESV in practice, a new control structure of two degree-of-freedom (DOF) μ synthesis control with the extended Kalman filter (EKF) considering actuators and sensors uncertainty is proposed, which constitutes a core support part of the paper. For the problem of sensors de-noising, an EKF is devised to provide a credible feedback signal to the two DOF μ controller. Aiming at the problem of reference command’s rapid change, one freedom feed forward is adopted, while another freedom output feedback is used to ensure good servo tracking as well as disturbance and noise rejection; furthermore to overcome the overshoot problem and acquire dynamic tuning, an integral is introduced in inner loop; additionally, two performance weighting functions are designed to achieve robustness and control energy limit considering the uncertainties in system. In order to verify the effectiveness of the designed two DOF μ synthesis controller with EKF, we suppose a typical engine test condition with Zoom-Climb and Mach Dash and consider time delay and Gaussian noise in the sensors. The simulation results show that the designed two DOF μ synthesis controller with EKF has good servo tracking and noise rejection performance and the relative steady-state and transient errors of temperature and pressure are both less than 0.1% and 0.2% respectively. Additionally, we validate the robust performance of the designed two DOF μ controller with EKF by using the upper bound value of the uncertainty parameters. Furthermore, to verify the advantage of the designed two DOF μ controller with EKF, we compare its control results with those of without EKF and μ controller without considering sensor time delay. The comparison results show that the designed two DOF μ controller with EKF provides better performance. Finally, to verify the advantage of μ synthesis controller, we designed a PID controller and compare the simulation result with μ controller, the comparison result show that the designed μ controller is better than PID controller.

PREDICTING UNKNOWN MOTION FOR MODEL INDEPENDENT VISUAL SERVOING

2001 ◽  
Vol 01 (03) ◽  
pp. 287-302 ◽  
Author(s):  
PATRICE WIRA ◽  
JEAN-PHILIPPE URBAN
Keyword(s):  
Kalman Filter ◽  
Time Delay ◽  
Real Time ◽  
Visual Servoing ◽  
Dimensional Space ◽  
Tracking Error ◽  
State Model ◽  
Divide And Conquer ◽  
Image Feature ◽  
Robot Arm

Prediction in real-time image sequences is a key-feature for visual servoing applications. It is used to compensate for the time-delay introduced by the image feature extraction process in the visual feedback loop. In order to track targets in a three-dimensional space in real-time with a robot arm, the target's movement and the robot end-effector's next position are predicted from the previous movements. A modular prediction architecture is presented, which is based on the Kalman filtering principle. The Kalman filter is an optimal stochastic estimation technique which needs an accurate system model and which is particularly sensitive to noise. The performances of this filter diminish with nonlinear systems and with time-varying environments. Therefore, we propose an adaptive Kalman filter using the modular framework of mixture of experts regulated by a gating network. The proposed filter has an adaptive state model to represent the system around its current state as close as possible. Different realizations of these state model adaptive Kalman filters are organized according to the divide-and-conquer principle: they all participate to the global estimation and a neural network mediates their different outputs in an unsupervised manner and tunes their parameters. The performances of the proposed approach are evaluated in terms of precision, capability to estimate and compensate abrupt changes in targets trajectories, as well as to adapt to time-variant parameters. The experiments prove that, without the use of models (e.g. the camera model, kinematic robot model, and system parameters) and without any prior knowledge about the targets movements, the predictions allow to compensate for the time-delay and to reduce the tracking error.

Engineered point spread functions for 3D parallel particle tracking of optically trapped particles

2011 ◽  
Author(s):  
Donald B. Conkey ◽  
Rahul P. Trivedi ◽  
Sri Rama Prasanna Pavani ◽  
Ivan I. Smalyukh ◽  
Rafael Piestun
Keyword(s):  
Particle Tracking ◽  
Point Spread Functions ◽  
Trapped Particles ◽  
Point Spread ◽  
Optically Trapped
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