scholarly journals Multi-innovation Self-tuning Kalman Filter with Unknown Parameters Systems

2015 ◽  
Author(s):  
Jun Yue ◽  
Ying Shi
Keyword(s):  
Kalman Filter ◽  
Unknown Parameters ◽  
Self Tuning

scholarly journals An Active Fault-Tolerant PWM Tracker for Unknown Nonlinear Stochastic Hybrid Systems: NARMAX Model and OKID-Based State-Space Self-Tuning Control

2010 ◽  
Vol 2010 ◽  
pp. 1-27 ◽  
Author(s):  
Chu-Tong Wang ◽  
Jason S. H. Tsai ◽  
Chia-Wei Chen ◽  
You Lin ◽  
Shu-Mei Guo ◽  
...  
Keyword(s):  
Kalman Filter ◽  
State Space ◽  
Active Fault ◽  
Stochastic Systems ◽  
Fault Tolerant ◽  
Initial Guess ◽  
Process Time ◽  
Parameter Estimates ◽  
Unknown Parameters ◽  
Self Tuning

An active fault-tolerant pulse-width-modulated tracker using the nonlinear autoregressive moving average with exogenous inputs model-based state-space self-tuning control is proposed for continuous-time multivariable nonlinear stochastic systems with unknown system parameters, plant noises, measurement noises, and inaccessible system states. Through observer/Kalman filter identification method, a good initial guess of the unknown parameters of the chosen model is obtained so as to reduce the identification process time and enhance the system performances. Besides, by modifying the conventional self-tuning control, a fault-tolerant control scheme is also developed. For the detection of fault occurrence, a quantitative criterion is exploited by comparing the innovation process errors estimated by the Kalman filter estimation algorithm. In addition, the weighting matrix resetting technique is presented by adjusting and resetting the covariance matrix of parameter estimates to improve the parameter estimation for faulty system recovery. The technique can effectively cope with partially abrupt and/or gradual system faults and/or input failures with fault detection.

scholarly journals Measurement Feedback Self-Tuning Weighted Measurement Fusion Kalman Filter for Systems with Correlated Noises

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Xin Wang ◽  
Shu-Li Sun
Keyword(s):  
Kalman Filter ◽  
Stochastic Systems ◽  
Tracking System ◽  
Weighted Least Squares ◽  
Full Rank ◽  
Global Optimality ◽  
Measurement Feedback ◽  
Innovation Model ◽  
Self Tuning ◽  
Noise Statistics

For the linear discrete stochastic systems with multiple sensors and unknown noise statistics, an online estimators of the noise variances and cross-covariances are designed by using measurement feedback, full-rank decomposition, and weighted least squares theory. Further, a self-tuning weighted measurement fusion Kalman filter is presented. The Fadeeva formula is used to establish ARMA innovation model with unknown noise statistics. The sampling correlated function of the stationary and reversible ARMA innovation model is used to identify the noise statistics. It is proved that the presented self-tuning weighted measurement fusion Kalman filter converges to the optimal weighted measurement fusion Kalman filter, which means its asymptotic global optimality. The simulation result of radar-tracking system shows the effectiveness of the presented algorithm.

Self-tuning Kalman filter for compensation of transmission delay and loss in line-of-sight guidance

2018 ◽  
Vol 233 (11) ◽  
pp. 4191-4201
Author(s):  
Akram Nikfetrat ◽  
Reza Mahboobi Esfanjani
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Random Variables ◽  
Measurement Model ◽  
Line Of Sight ◽  
Uncertain Parameters ◽  
Suggested Approach ◽  
Self Tuning ◽  
Simulation Results ◽  
Sequence Simulation

A self-tuning Kalman filter is introduced to reduce the destructive effects of the delayed and lost measurements in the guidance systems employing command to line-of-sight strategy. A sequence of Bernoulli distributed random variables with uncertain probabilities are used to model the delayed and lost observations. Besides the state estimation, the uncertain parameters of the measurement model are identified online using the covariance of innovation sequence. Simulation results are given to demonstrate the merits of the suggested approach.

Self-Tuning Fusion Kalman Filter for ARMA Signals

2012 ◽  
Vol 229-231 ◽  
pp. 1768-1771
Author(s):  
Wen Qiang Liu ◽  
Na Han ◽  
Man Yan ◽  
Gui Li Tao
Keyword(s):  
Kalman Filter ◽  
Instrumental Variable ◽  
Single Channel ◽  
Moving Average ◽  
Correlation Method ◽  
Autoregressive Moving Average ◽  
Model Parameters ◽  
Self Tuning ◽  
Optimal Fusion

For the single-channel autoregressive moving average (ARMA) signals with multisensor, and with unknown model parameters and noise variances, the local estimators of unknown model parameters and noise variances are obtained by the recursive instrumental variable (RIV) algorithm and correlation method, and the fused estimators are obtained by taking the average of the local estimators. Substituting them into the optimal fusion Kalman filter, a self-tuning fusion Kalman filter for single-channel ARMA signals is presented. A simulation example shows its effectiveness.

Chaos Synchronization in a Class of Chaotic Systems Using Kalman Filter and Feedback Linearization Methods

2006 ◽  
Author(s):  
Hassan Salarieh ◽  
Aria Alasty
Keyword(s):  
Kalman Filter ◽  
Feedback Linearization ◽  
High Performance ◽  
Chaotic Systems ◽  
Chaotic Behavior ◽  
Drive System ◽  
Unknown Parameters ◽  
Linearization Methods ◽  
Filter Approach ◽  
Different Chaotic Systems

In this paper a combination of Kalman filter and feedback linearization methods is used to present a controller-identifier system for synchronizing two different chaotic systems. The drive system has some unknown parameters which are supposed to have linear form within its dynamic equation. An identifier based on Kalman filter approach is designed to estimate the unknown parameters of the drive system, and simultaneously a feedback linearizing controller is used to synchronize the chaotic behavior of the response system with the drive chaotic system. The method proposed in this paper is applied to the Lure’ and the Genesio dynamic systems as the drive and response chaotic systems. The results show the high performance of the method to identify and synchronize two different chaotic systems with unknown parameters and in presence of noise.

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