Predictive Algorithm of Network Delay Based on Robust Kalman Filter

2014 ◽  
Vol 631-632 ◽  
pp. 937-940
Author(s):  
Fang He ◽  
Hui Zhang ◽  
Bing Bing Li
Keyword(s):  
Network Performance ◽  
Moving Average ◽  
Arma Model ◽  
Estimation Algorithm ◽  
Networked Control System ◽  
System Stability ◽  
Network Delay ◽  
Predictive Algorithm ◽  
Parameter Estimation Algorithm ◽  
Set Up

Due to irregular information flow for NCS (Networked Control System), network delay has performance of random and variability. It reduces system stability, network performance and control performance. This paper focuses on research of predictive algorithm of network delay. Network delay data is obtained in PROFIBUS-DP. Based on network delay data, the ARMA (Auto-Regressive and Moving Average) model of delay is set up. The parameter estimation algorithm of Robust Kalman is used to estimate parameters of proposed ARMA model of network delay. A simulation example is given and verifies efficiency of predictive algorithm proposed.

Prediction of Network Utilization Based on ARMA Model and RELS

2014 ◽  
Vol 945-949 ◽  
pp. 2780-2783 ◽  
Author(s):  
Hui Zhang ◽  
Fang He ◽  
Chun Yan Han
Keyword(s):  
Moving Average ◽  
Arma Model ◽  
Estimation Algorithm ◽  
Networked Control System ◽  
Model Parameters ◽  
Predictive Algorithm ◽  
Network Utilization ◽  
Parameter Estimation Algorithm ◽  
General Network ◽  
Auto Regressive

This paper focused on predictive algorithm of network utilization for networked control system (NCS). Auto-Regressive and Moving Average (ARMA) model was presented for general network utilization, which with fixed constant and known white noise. ARMA model parameters are estimated using parameter estimation algorithm of Recursive Extended Least Squares (RELS). Finally, a simulation example was given to realize RELS of ARMA model. Predictive output of network utilization can be obtained and converge to real state.

scholarly journals Least-Squares Parameter Estimation Algorithm for a Class of Input Nonlinear Systems

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Weili Xiong ◽  
Wei Fan ◽  
Rui Ding
Keyword(s):  
Parameter Estimation ◽  
Nonlinear Systems ◽  
Least Squares ◽  
Moving Average ◽  
Estimation Algorithm ◽  
Autoregressive Moving Average ◽  
Parameter Estimates ◽  
Persistent Excitation ◽  
Estimation Algorithms ◽  
Parameter Estimation Algorithm

This paper studies least-squares parameter estimation algorithms for input nonlinear systems, including the input nonlinear controlled autoregressive (IN-CAR) model and the input nonlinear controlled autoregressive autoregressive moving average (IN-CARARMA) model. The basic idea is to obtain linear-in-parameters models by overparameterizing such nonlinear systems and to use the least-squares algorithm to estimate the unknown parameter vectors. It is proved that the parameter estimates consistently converge to their true values under the persistent excitation condition. A simulation example is provided.

Decomposition based least squares iterative estimation algorithm for output error moving average systems

2014 ◽  
Vol 31 (4) ◽  
pp. 709-725 ◽  
Author(s):  
Wenge Zhang
Keyword(s):  
Least Squares ◽  
Design Methodology ◽  
Moving Average ◽  
Estimation Algorithm ◽  
Identification Algorithm ◽  
Iterative Identification ◽  
Iterative Estimation ◽  
Content Type ◽  
Engineering Computation ◽  
Parameter Estimation Algorithm

Purpose – The purpose of this paper is to solve the heavy computational problem of parameter estimation algorithm. Design/methodology/approach – Presents a decomposition least squares based iterative identification algorithm. Findings – Can estimate the parameters for linear or pseudo-linear systems and have lower computational burden. Originality/value – This paper adopts a decomposition technique to solve engineering computation problems and offers a potential and efficient algorithm.

scholarly journals Spectrum Based Power Management for Congested IoT Networks

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2681
Author(s):  
Kedir Mamo Besher ◽  
Juan Ivan Nieto-Hipolito ◽  
Raymundo Buenrostro-Mariscal ◽  
Mohammed Zamshed Ali
Keyword(s):  
Power Management ◽  
Network Performance ◽  
Channel Allocation ◽  
Battery Life ◽  
Packet Routing ◽  
Data Packets ◽  
Increasing Demand ◽  
Iot Devices ◽  
Set Up

With constantly increasing demand in connected society Internet of Things (IoT) network is frequently becoming congested. IoT sensor devices lose more power while transmitting data through congested IoT networks. Currently, in most scenarios, the distributed IoT devices in use have no effective spectrum based power management, and have no guarantee of a long term battery life while transmitting data through congested IoT networks. This puts user information at risk, which could lead to loss of important information in communication. In this paper, we studied the extra power consumed due to retransmission of IoT data packet and bad communication channel management in a congested IoT network. We propose a spectrum based power management solution that scans channel conditions when needed and utilizes the lowest congested channel for IoT packet routing. It also effectively measured power consumed in idle, connected, paging and synchronization status of a standard IoT device in a congested IoT network. In our proposed solution, a Freescale Freedom Development Board (FREDEVPLA) is used for managing channel related parameters. While supervising the congestion level and coordinating channel allocation at the FREDEVPLA level, our system configures MAC and Physical layer of IoT devices such that it provides the outstanding power utilization based on the operating network in connected mode compared to the basic IoT standard. A model has been set up and tested using freescale launchpads. Test data show that battery life of IoT devices using proposed spectrum based power management increases by at least 30% more than non-spectrum based power management methods embedded within IoT devices itself. Finally, we compared our results with the basic IoT standard, IEEE802.15.4. Furthermore, the proposed system saves lot of memory for IoT devices, improves overall IoT network performance, and above all, decrease the risk of losing data packets in communication. The detail analysis in this paper also opens up multiple avenues for further research in future use of channel scanning by FREDEVPLA board.

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