Self‐tuning weighted fusion Kalman filter for ARMA signal with colored measurement noise and its convergence analysis

Liu, Jinfang; Deng, Zili

doi:10.1002/acs.2277

Cited by 10 publications

(2 citation statements)

References 26 publications

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“…[12][13][14] The temperature of the clinker was initially measured using thermocouples in grate coolers in order to properly cool the clinker, and the air pressure was then adjusted. 15,16 However, this was ineffective since thermocouples or any other temperature sensor components could not withstand ambient temperature. 17,18 The clinker temperature profile along the full length of the cooler was also difficult to determine.…”

Section: Introductionmentioning

confidence: 99%

Optimum control of under‐grate pressure of clinker cooler by optimizing the proportional integral derivative controller parameters using honey badger algorithm technique

Brijet¹,

Suresh²

2023

Optim Control Appl Methods

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The minimum air pressure necessary at the grate to penetrate a bed of heated clinker of a certain thickness and permeability is known as under‐grate pressure. It is vital to keep the under‐grate pressure consistent because excessive pressure causes the clinker to form an unstable suspension and minimum pressure allows heat from the clinker to harm the cooler grates. So it is necessary to obtain excellent performance through reasonable control methods. For this purpose, this article proposes an optimization approach for a proportional integral derivative (PID) controller to control the pressure below the grate of the clinker cooler. The proposed optimization approach is the honey badger algorithm (HBA), which mimics the feeding habits of honey badgers. The main aim of proposed approach is to maintain and regulate under‐grate pressure of clinker cooler using HBA and provide optimum cooling rate to clinker. The proposed approach based grate cooler also provide the less electricity consumption and stable temperature. The proposed technique is implemented in MATLAB and its efficiency is compared to various existing techniques. From the simulation, the electricity consumption of proposed approach RMSE is 158 and mean accuracy is 98.65. Under‐grate pressure based RMSE is 5.01 and the mean accuracy become 99.55. The outlet temperature based RMSE of proposed approach is 4.32 and mean accuracy is 81.21. These values are lower than existing approaches, it reveals the proposed approach is better than the existing one.

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Section: Introductionmentioning

confidence: 99%

Optimum control of under‐grate pressure of clinker cooler by optimizing the proportional integral derivative controller parameters using honey badger algorithm technique

Brijet¹,

Suresh²

2023

Optim Control Appl Methods

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“…Compensating the linearization errors by introducing the fictitious noise [3] yields the unknown uncertain noise variances. Similarly, for systems with unmodeling dynamics, compensating Now, we investigate the asymptotic properties of the local and two-level hierarchical fusion robust time-varying Kalman filters, and we shall present the corresponding steady-state robust Kalman filters and also rigorously prove the convergence in a realization between the robust time-varying and steady-state Kalman filters, by the DESA method [30,31,37].…”

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confidence: 99%

Hierarchical fusion robust Kalman filter for clustering sensor network time‐varying systems with uncertain noise variances

Zhang

Deng

2013

Adaptive Control & Signal

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For the clustering time-varying sensor network systems with uncertain noise variances, according to the minimax robust estimation principle, based on the worst-case conservative system with conservative upper bounds of noise variances, applying the optimal Kalman filtering, the two-level hierarchical fusion timevarying robust Kalman filter is presented, where the first-level fusers consist of the local decentralized robust fusers for the clusters, and the second-level fuser is a global decentralized robust fuser for the cluster heads. It can reduce the communication load and save energy resources of sensors. Its robustness is proved by the proposed Lyapunov equation method. The concept of robust accuracy is presented, and the robust accuracy relations of the local, decentralized, and centralized fused robust Kalman filters are proved. Specially, the corresponding steady-state robust local and fused Kalman filters are also presented, and the convergence in a realization between the time-varying and steady-state robust Kalman filters is proved by the dynamic error system analysis method. A simulation example shows correctness and effectiveness. Figure 1. The two-level hierarchical clustering sensor network.A sensor network system consists of a number of sensor nodes distributed over a spatial region. Each node has sensing, communication, and computation capabilities, which is also called an agent. Usually, the number of sensor nodes is very large, and these nodes distribute in a remote area, so the cost of transmitting is higher than computation; hence it may be beneficial to partition the sensor nodes into groups (clusters) to reduce the communication load and save energy resources of sensor nodes, using the nearest neighbor rules [12], as shown in Figure 1. Each cluster has a cluster head (local fusion center) and a number of member nodes. Each member node sends its data or local estimator to the corresponding cluster head, and each cluster head sends its data or fused estimator to the base station (global fusion center) to obtain the global fused estimator. This is a hierarchical strategy with two levels, which decomposes a large sensor network into separate zones within which data processing and aggregation can be carried out locally.There exist two kinds of fusion methods, centralized and distributed fusion methods, depending on whether raw data are used directly for fusion or not [13]. The former can give the globally optimal state estimation by directly combining local measurement data to obtain an augmented measurement equation, but its disadvantage is that it requires a larger computational burden. The latter includes the distributed state fusion and distributed measurement fusion methods [14]. The distributed state fusion method can give the globally optimal or suboptimal state estimation by combining or weighting the local state estimators [15][16][17][18][19] whose advantages are that it can facilitate fault detection and isolation, and can reduce the computational burden. The distributed measurement fu...

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Self‐tuning fusion Kalman filter weighted by scalars and its convergence analysis for multi‐channel autoregressive moving average signals

Tao

Deng

2014

Adaptive Control & Signal

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SummaryFor the multi‐sensor multi‐channel autoregressive (AR) moving average signals with white measurement noises and an AR‐colored measurement noise, a multi‐stage information fusion identification method is presented when model parameters and noise variances are partially unknown. The local estimators of model parameters and noise variances are obtained by the multidimensional recursive instrumental variable algorithm and correlation method, and the fused estimators are obtained by taking the average of the local estimators. They have the strong consistency. Substituting them into the optimal information fusion Kalman filter weighted by scalars, a self‐tuning fusion Kalman filter for multi‐channel AR moving average signals is presented. Applying the dynamic error system analysis method, it is proved that the proposed self‐tuning fusion Kalman filter converges to the optimal fusion Kalman filter in a realization, so that it has asymptotic optimality. A simulation example for a target tracking system with three sensors shows its effectiveness. Copyright © 2014 John Wiley & Sons, Ltd.

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Self‐tuning weighted fusion Kalman filter for ARMA signal with colored measurement noise and its convergence analysis

Cited by 10 publications

References 26 publications

Optimum control of under‐grate pressure of clinker cooler by optimizing the proportional integral derivative controller parameters using honey badger algorithm technique

Optimum control of under‐grate pressure of clinker cooler by optimizing the proportional integral derivative controller parameters using honey badger algorithm technique

Hierarchical fusion robust Kalman filter for clustering sensor network time‐varying systems with uncertain noise variances

Self‐tuning fusion Kalman filter weighted by scalars and its convergence analysis for multi‐channel autoregressive moving average signals

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