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22
K =
q2
(19)
K02 = ¥1ш
We compose an equation of discharge for section lines I-I and II-II with border flows ml,m2, passing through dike heads [3]: - for right section
i - Y id - p>
(23)
Kmyehelisinai = hmlsrn(ad +ß0)(l - Psl)\udy (20)
- for left section
KmVMsina, = h„2sin(a + ßo)(1 - PJ\Udy (21)
hi
By executing integration in (20, 21) with the account of (1) after certain transformations we obtain
- for right section
K oi =¥Чш1 - for left section
I - f 1(1 - Pi
(22)
where a,and mean specific discharges qml,qm2 are determined by recommendations [3]; ^=0,55 — relative deficit velocity in Shlihting-Abramovich equation. Conclusion
Thus, the solution of the equation has been brought to end, the calculation is carried out in the following order:
- mean specific discharges qml,qm2 are determined according to recommendations [3, 7];
- using (22) and (23) we calculate streamline coefficients for right and left dikes and discharges passing through perforated sections Qm1,Qm 2, respectively;
- we calculate mean specific discharges q0, and q01, q02 by (18) and (16), and q03 by (12);
- we determine dynamic axes deflection by (7) and (8) and total deflection by (9).
References:
1. Altunin S. T. Регулирование русел. Сельхозиздат, - M., - 1962, 351 p.
2. Bakiev M. R. River bed regulation by cross combined dikes. XXIV Jahr congress Madrid a study of streams and water sheds of high hydravlic irregularity, 9-13 september, - 1991, MADRID/ESPANA.
3. Kodirov O. Совершенствование конструкций и разработка метода гидравлического расчета комбинированных дамб, author's abstract for Ph. D. dissertation, - Tashkent, - 1991.
4. Muradov R. A. Совершенствование конструкций и методов расчетного обоснования частично затопленных комбинированных дамб, author's abstract for Ph. D. dissertation, - Tashkent, - 1993.
5. Bakiev M. R., Togunova N. P. Гидравлический расчет сквозных шпор с переменной застройкой. Гидротехническое строительство - № 12, - 1989.
6. Abramovich G. N. Теория турбулентных струй, - M., - 1960, 716 p.
7. Urkinbaev R. K. Некоторые вопросы гидравлики сквозных шпор в условиях р. Амударьи, author's abstract for Ph. D. dissertation, -Tashkent, - 1969, 24 p.
Yusupbekov Nadirbek Rustambekovich Professor, Department of Automation of manufacturing processes, Tashkent State Technical University, Uzbekistan Gulyamov Shukhrat Manapovich Professor, Department of Automation of manufacturing processes, Tashkent State Technical University, Uzbekistan Ergashev Farkhod Arifjanovich Senior researcher, Department of Automation of manufacturing processes, Tashkent State Technical University, Uzbekistan Kabulov Nozimjon Abdukarimovich Senior Lecturer, Department of Automation of machine-building production, Andijan Mechanical Engineering Institute, Uzbekistan
Some objectives for constructing a model of intelligent control systems of dynamic objects
Abstract: Within conceptual modeling performed formalization of mathematical description of the intelligent control system in the dynamic objects and a priori incompleteness fuzzy initial information. The dynamic properties of the object are described by the apparatus of the state space, and intelligent operators realizing the perception, representation, the formation of concepts, judgments and inferences in the learning process are the formal means of the processing of data and knowledge, as well as the decision-making process in terms of interaction of intellectual system with the environment.
Keywords: intelligent control systems, intelligent transducer, decision support, interaction with the environment, a priori incompleteness and vagueness of the initial information.
Some objectives for constructing a model of intelligent control systems of dynamic objects
Artificial intelligence technology adopted attributed information technologies that provide the ability to handle knowledge and provide the following operations [1]:
- internal interpretability, which ensures identification of each information unit;
- structure, enabling recurring embeddability individual information units in each other;
- the establishment of a functional, explanatory, etc. types of relationships between information units;
- scalability, which implements the possibility of introducing different metrics for determining the quantitative, ordinal and other relations information;
- activity implements the ability to initiate action when new information becomes available;
- realization of classifying relations, generalizing patterns existing in this substantive area.
Intelligent system is a targeted system choosing the dominant task from a valid for this class system; the search for a solution to the problem; its decisions and that experience if it is necessary to change a valid class of tasks.
Application of intelligent control systems ensures the successful solution of tasks when a priori incompleteness and ambiguity of the original information, variability and inaccuracy of the studied characteristics of dynamic object, more effective decisionmaking in different situations of risk and possible conflicts.
General view of the automatic control system presented in Fig. 1.
Fig. 1. Block diagram of the control system: UC A -r - input signal; u - control object and input; d
An object can be thought of as a black box on the entry vector of parameters which controls actions C. Decision maker, expects to get some result-vector of states Y. Optimal control problem is determining using mathematical models of objects and systems of management D of such a control action to come as close to the desired result (vector Y ).
Formally, the intelligent system describes the following six:
T x X x S —— M xT, T x M x S ——^C xT, C xT x X x S a3 > R xT, T x X = {A xT }X xT + {B xT }U xT, T xY = {D xT }X xT, T x R xY --^C xT,
(1) (2)
(3)
(4)
(5)
(6)
control unit; PA - controller; CO - controlling object; - front outrage; y - output signal control object
where T-many points in time; X, S, M, C, R and Y-respectively a multitude of system States, environment, motivation, purpose of the projected and actual results; A,B,D matrix parameters; ai,a2,a3 and at consequently intellectual conversion operators.
In the description (1) (6) may be combined represent objects of the system in the form of a set of values or multiple statements, or any of the other forms.
The dynamic properties of the system are described in using state-space matrices parameters. Smart operators implement perception, representation, formation of concepts, judgments and inferences in the process of cognition are formal means of processing knowledge and information, as well as the decision-making process.
The process of interaction with the environment of intellectual system can be thought of as a process that involves the following input and output parameters (fig. 2.):
Fig. 2. System structure a - rx 1 vector file of resentment; z - signal model of the object; 0 - signal goal; u - signal control; Y - vector output effects on an object (a vector of intellectual status of the converter)
In General, under the intelligent transducer refers to some device, which is based on input signals y,z,u and signal that identifies the type of block extradition synthesized control (BEC) of regulation and bearing information that allows you to create free-
form enough regulation law [2]. Smart converter-some advanced block intellectual system that includes a dynamic expert system (DES) and decision-m aking block (DMB). In turn, the DES includes a knowledge base (KB), block peer review (BPR) and block
condition assessment (BCA) system, block execution control (BEC).
Signal control object model z contains information about the current condition of the structure and parameters for the system. Signal 0 contains information about the current state of the target.
In General, the control object are described by equations of the form [3]:
fx = f (x,U ,a, z ,t),
\ _____(7)
[y = c (x ), X (t o) = X 0,t > 10,
Here: x - (n xl) vector status; U - (m xl) vector control; y -(lxl) exit vector (dimension); c(•)-(1x1) the specified vector function; f (•) - (n x 1) vector function that provides the existence and uniqueness of the solution of the Koshi problem; z - (N x 1) the vector object's parameters.
When this
z = z0 + z, (8)
where is z0 controlled (given) component of the vector object's parameters (in the General case z0 = z"(t)) and z not a controlled respectively (unknown) component, which is defined on the basis of those or other identification methods and allows to form the signal models (z).
If the uncertainty of the object model can be reduced to parametric uncertainties in the model object is a vector z = z0 + z (where the vector z characterizes the uncertainty on parameters and structure).
In general, the transmitter represent a logic-dynamic device that processes incoming information on a current and generates a signal Y at a pace with the occurring processes. In smart transducer is implemented.
Y = F (x z) (9)
where is F(•) some operator, operating from space ^ in a p dimensional space ( YsS' ) to characterize the structure and algorithm of intelligent converter.
Compliance with this statement generates p x 1 vector Y, that determines, depending on the intended target, the environment, system state, actions on the object control, aimed at fulfilling this purpose.
We assume that an intelligent converter at each moment of time forms the current control goal in front of the object, whereby concrete is put the current task, and block generates the desired control algorithm that provides the current goals and is solution of the problem.
Thus, every time t > t0 the status vector converter provides information that can help you deliver and meet the challenge of controlling the object.
Outrage is an element of some specified in sets, i. e.:
aeW (t ),t > t0. (10)
Uncontrolled elements, as well as management also satisfy the conditions
z e L(t),t > t0, (11)
u e U(t),t > t0, (12)
where is some given many, respectively.
The purpose of the control object (1) in General, can be represented as the following restrictions:
,t)eQj(t),t > t0, (13)
where the vector status; -a neighborhood of some set, set in space (respectively); -specified vector function is continuously differen-tiable on all of the variables (the more common case where the operator is acting out), i. e., is some linear normed space.
Definition 1. Under the value of removing the element from the set in a sense measures proximity refers to the amount calculated according to the expression
p(f3,Q) = wnp{P>P) (14)
Definition 2. surroundings set in space there are so many elements that each item has been deleted from a myriad of not more than the amount (in the sense of the measures defined in the vicinity).
Definition 3. Measure proximity arbitrary elements represents some positively defined inand unlimited top functionality or scalar function.
In particular:
pe^HlA-p% (15)
where is the norm in space; or
p(p\p2 ) = ©((' -J32||). (16)
Here is a scalar function, positive positive values of the argument.
Given the magnitude of the define symbols entered removal of an arbitrary element from the set in accordance with a measure of proximity.
Given the ratio definitions entered for the purpose of control can be converted to an equivalent expression.
p (((x,t),Q)<e,t > t0. (17)
Then the management objective in enough general case can be formulated as follows: for the initial state of a control object, which may be an arbitrary element from the set, that is, you want to ensure the implementation ratio (17) for each if there are restrictions (10) (12).
Block management formulation in a fairly general case given the task implements ratio.
u = K(Y), (18)
where is some operator develop scoped control in space and in the values pane.
While the information contained in the vector must be sufficient for the synthesis ofblock management develop the required impact assessment in accordance with the current target generated intellectual converter.
2. 3.
References:
Ohtilev M. Y., Sosolov B. V., Yusupov R. M. Intelligent technologies monitoring and management of structural dynamics of complex technological objects-IZD-Vo, science; - M: 2006. - 410 p.
Pupkov K. A. Intelligent systems: problems of theory and practice//Izvestiya vuzov "Instrumentation", - № 9-10, 1994. - P. 5-7. Malomuzh T. I. Optimal control based on intelligent systems, AAEES, - № 1 (13), 2014. - P. 132-139.
