Compensating Random Time Delays in a Feedback Networked Control System With a Kalman Filter

2011 ◽  
Vol 133 (2) ◽  
Author(s):  
Alexander C. Probst ◽  
Mario E. Magaña ◽  
Oliver Sawodny
Keyword(s):  
Kalman Filter ◽  
Time Delay ◽  
Time Delays ◽  
Padé Approximation ◽  
Networked Control System ◽  
Random Time ◽  
Pade Approximation ◽  
Time Stamps ◽  
The Mean ◽  
Measurement And Control

This paper presents procedures to handle time delays in a feedback control loop in which both measurement and control signals are sent through a network, where random time delays occur. Even when time stamps are utilized, for example, the control signal time delay still must be estimated. Using a Padé approximation and a Kalman filter, we can estimate the mean time delay. Furthermore, methods are described to estimate the time delay of every packet instead of the mean for the measurement channel, which greatly enhances the performance of the Padé approximation approach. This is done by matching the measurements to the expected measurements provided by the filter with maximum likelihood. The methods are applied to an inverted pendulum to assess performance.

scholarly journals Moving Horizon Estimation for Cooperative Localisation with Communication Delay

2014 ◽  
Vol 68 (3) ◽  
pp. 493-510 ◽  
Author(s):  
Wei Gao ◽  
Jian Yang ◽  
Ju Liu ◽  
Hongyang Shi ◽  
Bo Xu
Keyword(s):  
Kalman Filter ◽  
Time Delay ◽  
Extended Kalman Filter ◽  
Acoustic Communication ◽  
Underwater Vehicle ◽  
Random Time ◽  
Computation Method ◽  
Moving Horizon Estimation ◽  
Underwater Environment ◽  
Simulation Results

Cooperative Localisation (CL) technology is required in some situations for Multiple Unmanned Underwater Vehicle (MUUVs) missions. During the CL process, the Relative Localisation Information (RLI) of the master UUV is transmitted to slave UUVs via acoustic communication. In the underwater environment, the RLI is subject to a random time delay. Considering the time delay characteristic of the RLI during the acoustic communication, a Moving Horizon Estimation (MHE) method with a Delayed Extended Kalman Filter (DEKF)-based arrival cost update law is presented in this paper to obtain an accurate and reliable estimation of present location. Additionally, an effective computation method for the MHE method is employed, in which the “Lower Upper” (LU) factorization is used to compute the solution of the Karush-Kuhn-Tucker (KKT) system. At the end of this paper, simulation results are presented to prove the superiority and practicality of the proposed MHE algorithm.

scholarly journals Care time Delays in Acute Coronary Syndromes with Persistent St Elevation (stem) and the Delaying Factors: Prospective STUDY About 50 Cases in the Cardiology Department of Aristide le Dante Hospital.

2020 ◽  
Vol 2 (1) ◽  
pp. 01-03
Author(s):  
Momar Dioum
Keyword(s):  
Time Delay ◽  
Time Delays ◽  
Time Trial ◽  
Coronary Intervention ◽  
Self Medication ◽  
Coronary Syndrome ◽  
Cardiology Department ◽  
St Elevation ◽  
The Mean ◽  
Mean Time

The care of acute coronary syndrome with persistent ST-elevation (STEMI) is a time-trial race: ‘‘time is myocardium”. The treatment relies on myocardial reperfusion by percutaneous coronary intervention (PCI) or fibrinolysis as promptly as possible. The main objective of this work was to assess the care time delays and the delaying factors during STEMI. We conducted a prospective, descriptive and analytic study over a 6 months’ time period. Were included all the patients received for STEMI. We have studied the care time delays and the delaying factors. We have compiled 50 patients. The mean age was 58.4 years and the sex-ratio M/F 2.5. The chest pain was typical in 39 patients. The mean time elapsed between the beginning of the pain and the first medical contact was 12 h 16 min. Transport (76%) and self-medication (70%) were the significant delaying factors found (p = 0.0001). The mean time elapsed between the first medical contact and the electrocardiogram was 9 h 57 min. The main factors delaying the diagnosis were the unavailability of the electrocardiogram device and the absence of electrocardiogram prescription (p = 0.001). The mean time elapsed between the electrocardiogram and the admission in the cardiology department was 3 h 02 min. The transport was the principal factor lengthening that time delay (p = 0.0001). Among the patients admitted directly in cardiology department, the mean time delay to perform the ECG was 30 min. The mean time delay of fibrinolysis was 2 h 11 min. Streptokinase shortage was the most frequent delaying factor (p = 0.001). The mean time delay between the qualifying ECG and the PCI completion was 2 h 42 min. The unavailability of the medical team was the first factor lengthening that time delay (p = 0.0001). The care time delays were lengthened enough in our context. This testifies to the lack of a codified strategy for STEMI care. It is essential to develop pre-hospital emergency medicine and sensitize the population and healthcare professionals.

Asymptotic Stability With Probability One of Random-Time-Delay-Controlled Quasi-Integrable Hamiltonian Systems

2016 ◽  
Vol 83 (9) ◽  
Author(s):  
R. H. Huan ◽  
W. Q. Zhu ◽  
R. C. Hu ◽  
Z. G. Ying
Keyword(s):  
Time Delay ◽  
Asymptotic Stability ◽  
Hamiltonian Systems ◽  
Hybrid System ◽  
Networked Control System ◽  
Random Time ◽  
Largest Lyapunov Exponent ◽  
Integrable Hamiltonian Systems ◽  
Markov Jump ◽  
Solution Property

A new procedure for determining the asymptotic stability with probability one of random-time-delay-controlled quasi-integrable Hamiltonian systems is proposed. Such a system is formulated as continuous–discrete hybrid system and the random time delay is modeled as a Markov jump process. A three-step approximation is taken to simplify such hybrid system: (i) the randomly periodic approximate solution property of the system is used to convert the random time delay control into the control without time delay but with delay time as parameter; (ii) a limit theorem is used to transform the hybrid system with Markov jump parameter into one without jump parameter; and (iii) the stochastic averaging method for quasi-integrable Hamiltonian systems is applied to reduce the system into a set of averaged Itô stochastic differential equations. An approximate expression for the largest Lyapunov exponent of the system is derived from the linearized averaged Itô equations and the necessary and sufficient condition for the asymptotic stability with probability one of the system is obtained. The application and effectiveness of the proposed procedure are demonstrated by using an example of stochastically driven two-degrees-of-freedom networked control system (NCS) with random time delay.

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