Modeling and Estimation of Flow Boiling System Using Kalman Filtering
الكلمات المفتاحية:
Induction machine، diagnostics، current spectrum، harmonicsالملخص
The Kalman filter (KF) is a set of mathematical equations that provides an efficient computational (recursive) means to estimate the state of a process, in a way that minimizes the mean of the squared error. The filter is very powerful in several aspects: it supports estimations of past, present, and even future states, and it can do so even when the precise nature of the modeled system is unknown. In this paper, the mathematical model of a continuous and discrete flow boiling system has been developed. Kalman filtering was used to estimate the states of a linearizead boiling system. Extended Kalman filtering (KF) was applied to estimate the states of the nonlinear boiling system. It follows from the obtained results that the KF and EKF do the job. KF gives a good result due to optimality and structure. Since it is difficult to install sensors inside the boiler, it will be more convenient to design a state feedback controller using the Kalman filter for the system to track some desired set points (e.g.. Temperature, pressure, etc). The results were presented using MATLAB-Simulink simulations.
المراجع
Fadel M.A.L. “Control of a continuous flow boiling system using Delta V software”, Project Laboratory II, BMGE, 2003.
Franks R.G.E. “Modelling and Simulation in Chemical Engineering”, Wiley- Interscience New York, pp.105, 1972.
Tyrone Vincent. ”Estimation Theory and Kalman Filtering EGGN519 lecture notice”, spring 2009.
Chui, Charles K.; Chen, Guanrong (2009). Kalman Filtering with Real-Time Applications. Springer Series in Information Sciences. 17 (4th ed.). New York: Springer.
Paul Zarchan; Howard Musoff (2000). Fundamentals of Kalman Filtering: A Practical Approach. American Institute of Aeronautics and Astronautics, Incorporated.
Gustafsson, Fredrik; Hendeby, Gustaf (2012). "Some Relations Between Extended and Unscented Kalman Filters". IEEE Transactions on Signal Processing. 2: 545–555.
Jinya Su; Baibing Li; Wen-Hua Chen (2015). "On existence, optimality and asymptotic stability of the Kalman filter with partially observed inputs". Automatica. 53: 149–154
Julier, Simon J.; Uhlmann, Jeffrey K. (1997). "A new extension of the Kalman filter to nonlinear systems" . Int. Symp. Aerospace/Defense Sensing, Simul. and Controls. Signal Processing, Sensor Fusion, and Target Recognition VI. 3: 182. Bibcode:1997SPIE.3068..182J.
Rudolph Emil Kalman. 1960. A New Approach to Linear Filtering and Prediction Problems.Transactions of the ASME–Journal of Basic Engineering 82, Series D (1960), 35–45.
K. Bergman. 2009. Nanophotonic Interconnection Networks in Multi-core Embedded Computing. In 2009 IEEE/LEOS Winter Topicals Meeting Series.
Connor Imes and Henry Hoffmann. 2016. Bard: A Unified Framework for Managing Soft Timing and Power Constraints. In International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation (SAMOS).
