Predictive Control of Electrical Equipment Reliability on the Basis of the Non-linear Canonical Model of a Vector Random Sequence

Author(s):  
Igor Atamanyuk ◽  
Vyacheslav Shebanin ◽  
Yuriy Kondratenko ◽  
Yuriy Volosyuk ◽  
Oleksii Sheptylevskyi ◽  
...  
2020 ◽  
Vol 27 (4) ◽  
pp. 309-313
Author(s):  
I. P. Atamanyuk ◽  
V. Y. Kondratenko ◽  
A. S. Kramarenko ◽  
O. E. Novikov ◽  
V. Y. Lykhach

Improvement of sow reproductive performance is a key factor determining the efficiency of the pig production cycle and profitability of pork production. This article presents the solution of an important scientific and practical problem of individual forecasting of sow reproduction . The population used for the present study is from a pig farm managed by the Limited Liability Company (LLC) ‘Tavriys’kisvyni’ located in Skadovsky district (Kherson region, Ukraine). The experimental materials used for this study consisted of 100 inds. of productive parent sows of the Large White breed.The litter size traits – the total number of piglets born (TNB), number of piglets born alive (NBA) and number of weaned piglets (NW) – were monitored in the first eight parities over an eleven year period (2007–2017). The method of the forecasting of sow litter size is developed based on the non-linear canonical model of the random sequence of a litter size change. The proposed method allows us to take maximum account of stochastic peculiarities of sow reproductive performance and does not impose any restrictions on the random sequence of a litter size change (linearity, stationarity, Markov property, monotony, etc.). The block diagram of the algorithm presented in this work reflects the peculiarities of calculation of the parameters of a predictive model. The expression for the calculation of an extrapolation error allows us to estimate the necessary volume of a priori and a posteriori information for achieving the required quality of solving the forecasting problem. The results of the numerical experiment confirmed the high accuracy of the proposed method of forecasting of sow reproduction. The method offered by us almost doubles the accuracy of forecasting of sow litter size compared to the use of the Wiener and Kalman methods. Thus, average forecast error decreases across the range of features TNB (1.71), NBA (1.68) and NW (1.25 piglets). Apparently, this may reflect a higher level of manifestation of the genetically determined level of individual sow fertility at the moment of piglet weaning. The higher adequacy of the developed mathematical model with regard to NW can be also due to the fact that the relations between sow litter size in different farrowings primarily have a non-linear character, which is taken into maximum account in our offered model. Given non-linearity, on the other hand, turns out to be a significant factor determining a lower estimation of the repeatability value for NW compared to the estimations for TNB and NBA. The use of the developed method will help to improve the efficiency of pig farming.


1999 ◽  
Vol 72 (10) ◽  
pp. 919-928 ◽  
Author(s):  
B. Kouvaritakis ◽  
M. Cannon ◽  
J. A. Rossiter

Author(s):  
Zhi Qi ◽  
Qianyue Luo ◽  
Hui Zhang

In this paper, we aim to design the trajectory tracking controller for variable curvature duty-cycled rotation flexible needles with a tube-based model predictive control approach. A non-linear model is adopted according to the kinematic characteristics of the flexible needle and a bicycle method. The modeling error is assumed to be an unknown but bounded disturbance. The non-linear model is transformed to a discrete time form for the benefit of predictive controller design. From the application perspective, the flexible needle system states and control inputs are bounded within a robust invariant set when subject to disturbance. Then, the tube-based model predictive control is designed for the system with bounded state vector and inputs. Finally, the simulation experiments are carried out with tube-based model predictive control and proportional integral derivative controller based on the particle swarm optimisation method. The simulation results show that the tube-based model predictive control method is more robust and it leads to much smaller tracking errors in different scenarios.


2019 ◽  
Vol 123 ◽  
pp. 184-195 ◽  
Author(s):  
S.O. Hauger ◽  
N. Enaasen Flø ◽  
H. Kvamsdal ◽  
F. Gjertsen ◽  
T. Mejdell ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document