Unscented Kalman filter based nonlinear state estimation case study — Nonlinear process control reactor (Continuous stirred tank reactor)

Shyamalagowri, M.; Rajeswari, R.

doi:10.1109/isco.2016.7727083

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…It is to be noted that the IoT sensor data consists of complex time series data, and thus requires efficient data analysis mechanisms. The authors in [73,74] presented a data analytic framework that involved a multi-dimensional feature handling, selection and extraction model. The papers also discussed the dynamic data analytic model for IoT sensor data prediction.…”

Section: Discussionmentioning

confidence: 99%

An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques

Krishnamurthi

Kumar

Gopinathan

et al. 2020

Sensors

224

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In the recent era of the Internet of Things, the dominant role of sensors and the Internet provides a solution to a wide variety of real-life problems. Such applications include smart city, smart healthcare systems, smart building, smart transport and smart environment. However, the real-time IoT sensor data include several challenges, such as a deluge of unclean sensor data and a high resource-consumption cost. As such, this paper addresses how to process IoT sensor data, fusion with other data sources, and analyses to produce knowledgeable insight into hidden data patterns for rapid decision-making. This paper addresses the data processing techniques such as data denoising, data outlier detection, missing data imputation and data aggregation. Further, it elaborates on the necessity of data fusion and various data fusion methods such as direct fusion, associated feature extraction, and identity declaration data fusion. This paper also aims to address data analysis integration with emerging technologies, such as cloud computing, fog computing and edge computing, towards various challenges in IoT sensor network and sensor data analysis. In summary, this paper is the first of its kind to present a complete overview of IoT sensor data processing, fusion and analysis techniques.

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Section: Discussionmentioning

confidence: 99%

An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques

Krishnamurthi

Kumar

Gopinathan

et al. 2020

Sensors

224

View full text Add to dashboard Cite

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“…A Model Predictive Controller used for control of temperature, concentration, and pH without a neural strategy for CSTR as presented by (Arivalagan et al, 2015;Shyamalagowri and Rajeswari, 2013;Hong and Cheng, 2012;Balaji and Maheswari, 2012). The MPC Controller block shown in Fig.…”

Section: Model Predictive Controller (Mpc)mentioning

confidence: 99%

Mathematical modeling and simulation of control strategies for continuous stirrer tank reactor

Ibrahim¹

2022

Bangladesh J. Sci. Ind. Res.

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This study aims to establish a mathematical model for the Continuous Stirred Tank Reactor (CSTR) reactor that exhibits highly nonlinear dynamics and was carried out implemented by model-based conventional and non-conventional controllers for temperature control. The developed controllers were Proportional, Proportional-Integral, Proportional-Derivative, Proportional-Integral-Derivative, Two Degrees of Freedom, and Model Predictive Controller. Then, the controllers were simulated, tuning, and optimized using Matlab®/Simulink®. The response results were compared and the analysis performed. The results indicated that the performance of 2-DOF-PID and MPC controllers is better than other conventional controllers for nonlinear systems such as the CSTR process. Bangladesh J. Sci. Ind. Res. 57(3), 149-162, 2022

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“…In Eqs. ( 5) and ( 6), which are obtained by an unscented transform method [15]. The state variables for the unscented conversion represent as ,, xy , straight speed, rotation speed, and19 sensor distance information.…”

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confidence: 99%

An Indoor Mobile Robot Localization in perspective of Analysis and Performance using Unscented Kalman Filter

Rashid

et al. 2022

Trends Sci

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This paper describes a method in an indoor environment for the estimation and position, using an Unscented Kalman Filter (UKF). The UKF algorithm applied for the position estimation proposing a new measurement uncertainty model that fixes the error covariance according to the distance measurement. In addition, this approach sets the non-diagonal component of the error covariance matrix for the uncertainty of the speed information and the measurement uncertainty to a value other than zero. This method is evaluated through an experiment using a wheel-type mobile robot with an LRF sensor in an indoor environment. In this experiment, we differentiate the estimation execution of the proposed approach with a conventional method that does not employ an adaptive uncertainty model. Moreover, the results improved the estimation performance by setting the non-diagonal component of the error covariance to a value other than zero. The main emphasis of this paper is to implement a practical UKF method for location estimation of a mobile robot and analyze it with better performance.

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Unscented Kalman filter based nonlinear state estimation case study — Nonlinear process control reactor (Continuous stirred tank reactor)

Cited by 6 publications

References 10 publications

An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques

An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques

Mathematical modeling and simulation of control strategies for continuous stirrer tank reactor

An Indoor Mobile Robot Localization in perspective of Analysis and Performance using Unscented Kalman Filter

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