Slipping detection and avoidance based on Kalman filter

Cavallo, Alberto; Maria, G. De; Natale, Ciro; Pirozzi, Salvatore

doi:10.1016/j.mechatronics.2014.05.006

Cited by 27 publications

(12 citation statements)

References 27 publications

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“…Furthermore, since satisfies property P1 while (⋅) is odd, the matrix G 2 ≜ E{Ψ( )Ψ T ( )} will be diagonal with elements G 2 ii = E{ 2 ( i )}. Analogous to the second expectation in (13) it is found that third expectation is.…”

Section: Joint Estimation Algorithm For Systems With Component and Sesupporting

confidence: 61%

“…It is easy to show that the fourth member in (13) is E{KΨ( )Ψ T ( )K T } = KG 2 K T . Finally, the expression of a posteriori covariance (13)…”

Section: Joint Estimation Algorithm For Systems With Component and Sementioning

confidence: 99%

“…(from the SA-estimator min-max results, see [35][36][37] ), the expression for a posteriori covariance (13)…”

Section: Joint Estimation Algorithm For Systems With Component and Sementioning

confidence: 99%

“…[4][5][6][7][8] Precise knowledge of system parameters and states is crucial for successful realization of many control techniques. Many modern engineering applications such as autonomous vehicles, 9 microphone sensing, 10 strain prediction for fatigue, 11 maintaining security of cyber-physical systems, 12 or robotic manipulation tasks 13 require real-time Kalman filtering framework with linear models.…”

Section: Introductionmentioning

confidence: 99%

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State and parameter joint estimation of linear stochastic systems in presence of faults and non‐Gaussian noises

Stojanović

Zhang

2020

Intl J Robust & Nonlinear

110

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Joint estimation of states and time-varying parameters of linear stochastic systems is of practical importance for fault diagnosis and fault tolerant control. The known fact is that measurements have outliers. They can significantly degrade the properties of linearly recursive algorithms, which are designed to work in presence of Gaussian noises. This article proposes two kinds of strategies for joint parameter-state robust estimation of linear stochastic models in presence of all possible faults and non-Gaussian noises. In the form of Theorem, joint robust algorithm for systems with sensor and component faults, as well as the algorithm for systems with parameter faults are proposed. Because of their good features in robust filtering, Masreliez-Martin filter represents a cornerstone for realization of the proposed robust algorithms for joint state-parameter estimation. The good features of proposed robust estimation algorithms, in relation to algorithms based on other widely-used filters, are illustrated by simulation results. On the other side, intensive research in the field of mathematical modeling of pneumatic servo drives has shown that their mathematical models are nonlinear in which a lot of important details cannot be included in the model. Also, it has been well known that the nonlinear model can be approximated by a linear model with time-varying parameters. Due to the abovementioned reasons, it can be assumed that the pneumatic cylinder model is a linear stochastic model with variable parameters. The good practical values of the proposed robust joint algorithm to identification of the pneumatic cylinder are illustrated by experimental results.

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Section: Joint Estimation Algorithm For Systems With Component and Sesupporting

confidence: 61%

“…It is easy to show that the fourth member in (13) is E{KΨ( )Ψ T ( )K T } = KG 2 K T . Finally, the expression of a posteriori covariance (13)…”

Section: Joint Estimation Algorithm For Systems With Component and Sementioning

confidence: 99%

“…(from the SA-estimator min-max results, see [35][36][37] ), the expression for a posteriori covariance (13)…”

Section: Joint Estimation Algorithm For Systems With Component and Sementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

State and parameter joint estimation of linear stochastic systems in presence of faults and non‐Gaussian noises

Stojanović

Zhang

2020

Intl J Robust & Nonlinear

110

View full text Add to dashboard Cite

show abstract

“…The process of Kalman filter is introduced in [20]. According to theory, the Kalman optimal gain matrix formula (15) can be obtained as [21]:…”

Section: 2 Attenuation Memory Filtering Algorithmmentioning

confidence: 99%

An innovative information fusion method with adaptive Kalman filter for integrated INS/GPS navigation of autonomous vehicles

Liu

Fan

et al. 2018

Mechanical Systems and Signal Processing

232

113

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Information fusion method of INS/GPS navigation system based on filtering technology is a research focus at present. In order to improve the precision of navigation information, a navigation technology based on Adaptive Kalman Filter with attenuation factor is proposed to restrain noise in this paper. The algorithm continuously updates the measurement noise variance and processes noise variance of the system by collecting the estimated and measured values, and this method can suppress white noise. Because a measured value closer to the current time would more accurately reflect the characteristics of the noise, an attenuation factor is introduced to increase the weight of the current value, in order to deal with the noise variance caused by environment disturbance. To validate the effectiveness of the proposed algorithm, a series of road tests are carried out in urban environment. The GPS and IMU data of the experiments are collected and processed by dSPACE and MATLAB/Simulink. Based on the test results, the accuracy of the proposed algorithm is 20% higher than that of a traditional adaptive Kalman filter. It also shows that the precision of the integrated navigation can be improved due to the reduction of the influence of environment noise.

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Force/Tactile Sensors Based on Optoelectronic Technology for Manipulation and Physical Human–Robot Interaction

Cirillo

Maria

et al. 2016

Advanced Mechatronics and MEMS Devices II

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Slipping detection and avoidance based on Kalman filter

Cited by 27 publications

References 27 publications

State and parameter joint estimation of linear stochastic systems in presence of faults and non‐Gaussian noises

State and parameter joint estimation of linear stochastic systems in presence of faults and non‐Gaussian noises

An innovative information fusion method with adaptive Kalman filter for integrated INS/GPS navigation of autonomous vehicles

Force/Tactile Sensors Based on Optoelectronic Technology for Manipulation and Physical Human–Robot Interaction

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