Online Estimation of Missing Data Using Sparse Optimization Techniques With Applications to Classical Control

Perepu, Satheesh K.; Tangirala, Arun K.

doi:10.1109/tcst.2017.2775191

Cited by 7 publications

(9 citation statements)

References 31 publications

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“…Compensator Controller Consider that the use of overcomplete dictionary to estimate missing data given by [23,24,26], it can take into account the features of the output signal. And it is constructed from functions corresponding to different features of the signal, such as oscillatory, transients and discontinuities, which is typically constructed by merging a set of the sparse bases in which the concerned signal has a sparse representation.…”

Section: Buffermentioning

confidence: 99%

“…Then the computation time of the method needs to be considered to avoid increasing the added delay in NCS. Even if the computation time of method in [23] is smaller at the beginning, with the continuous expansion of the matrix in the method, it will become larger, which cannot be ignored for NCS. In order to solve the problem, [24] proposed a sequential CS framework in which only part of the data packets before the current instant are needed.…”

Section: Introductionmentioning

confidence: 99%

“…In order to solve the problem, [24] proposed a sequential CS framework in which only part of the data packets before the current instant are needed. At the same time, the method of predicting one data packet of a single instant in [23] is extended to predict more data packets of multiple instants in [24]. Then the number of execution of the algorithm is reduced especially when the number of continuous packet loss is relatively large.…”

Section: Introductionmentioning

confidence: 99%

“…Researchers used the CS technique as a new predictive method to compensate the missing data, but their proposed algorithms can only estimate the missing data of single-output system [23,24]. In the actual engineering environment, most systems have multiple sensors to get more outputs, which are multi-output systems.…”

Section: Introductionmentioning

confidence: 99%

“…In the actual engineering environment, most systems have multiple sensors to get more outputs, which are multi-output systems. Under such circumstances, the compensated method in [23,24] cannot be used again. The only published work [20], deals with CS core idea for packet drops of multi-output NCS, which introduces the CS method as a compensated method.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Compensated methods for networked control system with packet drops based on compressed sensing

Fan

Yin

Zhenfei

et al. 2021

J. of Syst. Eng. Electron.

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Due to unreliable and bandwidth-limited characteristics of communication link in networked control systems, the realtime compensated methods for single-output systems and multioutput systems are proposed in this paper based on the compressed sensing (CS) theory and sliding window technique, by which the estimates of dropping data packets in the feedback channel are obtained and the performance degradation induced by packet drops is reduced. Specifically, in order to reduce the cumulative error caused by the algorithm, the compensated estimates for single-output systems are corrected via the regularization term; considering the process of single-packet transmission, a new sequential CS framework of sensor data streams is introduced to effectively compensate the dropping packet on single-channel of multi-output systems; in presence of the medium access constraints on multi-channel, the communication sequence for scheduling is coupled to the algorithm and the estimates of the multiple sensors for multi-output systems are obtained via the regularization term. Simulation results illustrate that the proposed methods perform well and receive satisfactory performance.

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Section: Buffermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Compensated methods for networked control system with packet drops based on compressed sensing

Fan

Yin

Zhenfei

et al. 2021

J. of Syst. Eng. Electron.

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Dynamical Soft Sensors from Scarce and Irregularly Sampled Outputs Using Sparse Optimization Techniques

Pinnamaraju

Tangirala

2023

Ind. Eng. Chem. Res.

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In process industries, quality variables such as concentrations and viscosity usually require offline laboratory analysis due to difficulties associated with online sensing and are often sampled slowly or irregularly compared to other variables such as temperatures and flow rates. Dynamical soft sensors, which relate the uniformly fast sampled variables to irregularly sampled quality variables, are crucial in control and process monitoring applications. Most identification approaches for soft sensing assume that all the variables are sampled regularly and uniformly. The existing auxiliary model (AM)-based approaches for dealing with irregular sampling suffer from drawbacks such as nonconvex optimization, lack of model parsimony and require prior information about the system dynamics. This work addresses a few of these issues by developing a flexible AM that can accommodate complex linear process dynamics without assuming any prior knowledge and compromising on model parsimony by using redundant Laguerre filters and casting the model learning in the sparse optimization framework. The developed AM is utilized to efficiently reconstruct the measurements at the base sampling interval, which further serves as a foreground for the traditional parametric model identification techniques or the expectation maximization algorithm to obtain optimal parameter estimates. The efficacy of the proposed AM-based soft sensor algorithm is demonstrated through synthetic as well as industrial simulation case studies. Finally, a few guidelines on effective sampling of quality variables to generate informative experiments for soft sensor development are also presented.

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An Integrated Implementation Methodology of a Lifecycle-Wide Tracking Simulation Architecture

et al. 2018

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Online Estimation of Missing Data Using Sparse Optimization Techniques With Applications to Classical Control

Cited by 7 publications

References 31 publications

Compensated methods for networked control system with packet drops based on compressed sensing

Compensated methods for networked control system with packet drops based on compressed sensing

Dynamical Soft Sensors from Scarce and Irregularly Sampled Outputs Using Sparse Optimization Techniques

An Integrated Implementation Methodology of a Lifecycle-Wide Tracking Simulation Architecture

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