Auxiliary particle filter-model predictive control of the vacuum arc remelting process
نویسندگان
چکیده
منابع مشابه
Liquid metal flow behavior during vacuum consumable arc remelting process for titanium
125301-7474 IJET-IJENS @ February 2012 IJENS I J E N S Abstract—To better understand the vacuum arc remelting (VAR) process, a 3D finite element model is established to analyzing the electromagnetic, temperature and flow fields of titanium alloy ingot during steady state melting process using ANSYS software. The research results show that the current flows from the crucible to consumable electr...
متن کاملModern Control Strategies for Vacuum Arc Remelting of Segregation Sensitive Alloys
There are several process variables which are crucial to the control of vacuum arc remelting of segregation sensitive alloys. These are: electrode gap, melt rate, cooling rate, furnace annulus, furnace atmosphere and electrode quality (i.e. cleanliness and integrity). Of these variables, active, closed loop control is usually applied only to electrode gap. Other variables are controlled by cont...
متن کاملUnscented Auxiliary Particle Filter Implementation of the Cardinalized Probability Hypothesis Density Filters
The probability hypothesis density (PHD) filter suffers from lack of precise estimation of the expected number of targets. The Cardinalized PHD (CPHD) recursion, as a generalization of the PHD recursion, remedies this flaw and simultaneously propagates the intensity function and the posterior cardinality distribution. While there are a few new approaches to enhance the Sequential Monte Carlo (S...
متن کاملImproved Optimization Process for Nonlinear Model Predictive Control of PMSM
Model-based predictive control (MPC) is one of the most efficient techniques that is widely used in industrial applications. In such controllers, increasing the prediction horizon results in better selection of the optimal control signal sequence. On the other hand, increasing the prediction horizon increase the computational time of the optimization process which make it impossible to be imple...
متن کاملAuxiliary Particle Implementation of the Probability Hypothesis Density Filter
Optimal Bayesian multi-target filtering is, in general, computationally impractical due to the high dimensionality of the multi-target state. Recently Mahler, [9], introduced a filter which propagates the first moment of the multi-target posterior distribution, which he called the Probability Hypothesis Density (PHD) filter. While this reduces the dimensionality of the problem, the PHD filter s...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IOP Conference Series: Materials Science and Engineering
سال: 2016
ISSN: 1757-8981,1757-899X
DOI: 10.1088/1757-899x/143/1/012018