Abstract:The paper presents an approach for program realization on managing system with combination of conventional realization and fuzzy logic. There is build a program model and offered language which allows describing  the control algorithm.

 Keywords: fuzzy logic, fuzzy controllers, membership functions, rule base
 

1. INTRODUCTION

 Conventional approaches for fuzzy controllers realization is difficult to determined and reported  the dynamics of  controlled object [6].
 The classical solution of the control language is extended toward processing of fuzzy nature events, according to the rules and membership function, defined from the user. The hybrid controller released as microprocessor system mixes the abilities of free programmable controllers with fuzzy controllers There are integrated three processing blocks:
· Peripheral processing (PP);
· Rules processing (RP);
· Interface processing (IP).
 It has common data base (DB), divided into several areas:
· Process variables (PV);
· Membership function (MF);
· Flag bytes (FB);
· Timer counter (TC);
· Rules base (RB);
· User function (UF).
 Each of them defines the types of the variables, using of the control program. The variables must be declared before their use.
 Each of the processing blocks is build as autonomy driver, activated by program events or clock interrupt.
 In conventional free programmable controllers realization is difficult to determined dead zone for each of separated input values and the control algorithms consist many contradictory conditions.
 The offered controller has some advantages over the classic realization. The Hybrid controller combines possibility of the free programmable controllers and  possibility of the fuzzy controllers .
 The block diagram of the hybrid controller shown on figure 1.

 The offered system, shown on figure 1, makes preprocessing on input information and forms logical inputs to the fuzzy  block (out1-outm) and to the control algorithms block (outm+1-outn).  Fuzzy block contains separated rule bases and forms logical outputs for each of operators. This block makes fuzzyfication, inference and defuzzyfication on input information and forms output signals to the periphery modules connected to the executive devices (out1-outp). The all physically inputs are distributed by control block (input preprocessing subsystem) and recorded into corresponded data base.  Control algorithms block contains drivers control programs and forms output signals to the periphery modules (outp+1-outr).

2. LANGUAGE DESCRIPTION

 The language has modular structure. There are three types of operators:
· declarative;
· executive;
· data definitions.
 The basic of them are rule-operators, interface operators and exception-operators
 The rule has common syntactically presentation as IF… THEN… operator. The general form of the rule is:

(1)                                                                         Rule number  IF  conditional part  THEN  action part;

 After the IF clause, it’s placed the condition, as disjunctive from of logical compares. Each compare has the general format:

(2)                                                                           A=B;

 where:
· A can be PV (process variables), FB (flag bytes) or TC(timer counter) type variables;
· B can be constant value, or variables of type MF (membership function), FB (flag bytes) or TC(timer counter).
 After THEN clause, it’s place the action part. The action has general format:

(3)                                                                        A1=B1:A2=B2:………….:An=Bn

 where:
· n is number of compares into the rule.

 Each of Ai can be PV (process variables), TC(timer counter) or FB (flag bytes) type. Ai may be a reserved words /EXEC/ or /RULE/ too. At these causes the controller must be execute the user function or change the normal sequence of the rules.
 Each of Bi can be PV (process variables), numerical or address constant. The address constant are logical names, corresponding to the rule number into RB(rules base) or UF(user function).
 For each compare, this procedure calculates the performance coincidence of the condition separately If the compare includes input membership function, the calculation is done, base on classically min-max. Other types of compare gives 0 or 1, if the result is "true"or "false"
 Each of rule is executed at two program steps:
· rule inference / calculating of the rule condition and forming of rile inference degree for the current rule/;
· executing of the rule action.

3. INFERENCE METHOD

 The inference of the rule is processed based on classical MAX-MIN algorithm.
 It is offered modification of MAX-MIN processing. It is based on definition of the evaluation of distance between  deferent values of input signals. Special function - D, distance is defined and added to the description of the membership functions. This function determinate the calculation of fuzzy operation "=" (compare), between two fuzzy sets.
 Let U is universe set, defined as:

(4)                                                                                                                  U : {0..4095} ;

 A and B are fuzzy sets, with member functions a(u) and b(u), for u belonging to U.
 The fuzzy operation A=B defined fuzzy logically function fA=B(u), determinate as:

    max u=D(x,y)min(a(x), b(y))
    0 if u not equal on D(x, y) for each pair x,y belonging U
    u, x and y belonging to U

 D(x,y) is distance between x and y and is user defined.
 The measure of ‘truth’ and distance between x and y are integers from 0 to 4095, results of the evaluation of D(x,y) or fA=B.
 For example:

(5)                                                                                                                 
 
                                                                                                             x,y belonging to U

 For this choice, the distance D(x,y) is equally to 4095, only if x=y and equally to 0 if   /large distance/.
 The inference MIN operation works with each of the local results of the rule compares, using fuzzy extension of the "logically end"
 After the inference of the all rules is done, defuzzyfication is made, using the gravity method. The defuzzyfication processes only these of PV (process variables), which are modified by any rule. The FB (flag bytes) consist binary or logic information and they aren’t defuzzyficated.
 The periphery processing includes the reading of the input channels, the recording into corresponding data fields and the writing the data fields into outputs channels [1,5]. The linking information for periphery processing is getting from the declaration part of the control program
 The user interface is supported by Interface processing block. The communication between the controller and Operator monitor is based on user-friendly logical protocol. The Operator station is based on IBM PC and includes the utilities, forming Integrated User Media.
 The described model is applied for the design of mix-tank controller, working in hydroponics Green house [3]. The difficulties of the control in this area come from impossibility for accurate measuring of parameters of the mixing reactive, the fluid, temperature, the results mixing pH and EC values [3].  In this case used six different operations for Green house control. Any of them has own data base and rule base. Input and output setting realizing the control algorithm structured as separated tasks: temperature control, ventilation control, meteorology control, illumination, water control and heating control. For this application describing model shown on fig .1  is modified Figure 2 illustrate this modification. The operation station is based on IBM PC and supports two modes:
· operator (user) interface - for local configuration of the technological parameters;
· system integrator interface - for access to all system resources

4. CONCLUSION

 The paper presents  one approach for design of hybrid controller, using fuzzy logic, for industrial applications. The classically inference procedure is modified by adding of distance functions allowing to building different compare functions between fuzzy sets.
 The controller released as microprocessor system mixes the abilities of free programmable controllers with fuzzy controllers.
 The using of real - time abilities of software and hardware realization allows to the user to control different types of  objects.
 The offered control system has some advantages over the classic realization. The precise and intelligence executing of the control action gives possibility for a solution of the control tasks by saving energy resources and makes this realization productive and efficient.

REFERENCES

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