JPH04291603A - Fuzzy control system - Google Patents

Abstract

PURPOSE:To obtain a fuzzy control system capable of sharply shortening an inference time and easily attaining simultaneous use with a feedback type fuzzy control rule. CONSTITUTION:In a fuzzy control system for individually finding out the status forecasting value of a controlled system in each temporary manipulated variable in a fuzzy model based upon the input of a past manipulated variable to the controlled system and the observation values of past and current system status variables, evaluating respective adaptation U1 to ON between respective forecasted values and control set points by fuzzy rules for target evaluation and determining an optimum manipulated variable by means of the adaptation U1 to UN, a condition that temporary manipulated variables are mutually primes is added, relating rules out of fuzzy model rules are grouped in each applied temporary manipulated variable to previously set up the temporary manipulated variable.

Description

[Detailed description of the invention]

[Object of the invention]

0002

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a fuzzy inference method used in a general-purpose fuzzy control controller.

0003

2. Description of the Related Art Recently, many attempts have been made to incorporate fuzzy control into control objects whose systems are difficult to model mathematically. Currently, there are two main ways to use fuzzy rules for control:

(1) Create a fuzzy model of the operator by converting the operator's empirical rules into rules and analyzing actual operation history data, and realize control similar to that of the operator. do.

(2) Using a target model (such as a fuzzy model), predict the future state when a certain temporary operation amount is given, and use fuzzy inference to evaluate the extent to which the predicted value satisfies the target. determine the optimal amount of operation.

The method (1) is the most commonly used method in fuzzy control and can be easily formulated into rules. However, since the rules include the operator's overall control strategy, each rule has a complex interaction. For this reason, simply arranging the operator's control knowledge in parallel often does not function effectively as it is.
Furthermore, it is often the case that the entire rule must be redesigned because one part of the rule has been changed. Furthermore, if the purpose of control changes, the rules must be completely rewritten.

In this regard, in the control method (2), the fuzzy model rules that indicate the ambiguity of the target system and the target evaluation rules that indicate the ambiguity included in the part that determines the control strategy are used. Since they can be clearly distinguished, it is easy to formulate adjustment and design policies. Further, any control target can be easily changed by simply changing the target fuzzy set given to the target evaluation rule. Hereinafter, this will be referred to as a state predictive fuzzy control method.

FIG. 2 shows a block diagram of a conventional state prediction type fuzzy control system. However, a fuzzy model is used here. In FIG. 2, 1 is a part that gives the temporary manipulated variable to the model, 2 is a fuzzy model for predicting the state, 3 is a fuzzy inference part that evaluates the predicted value, and 4 is a composite of the temporary manipulated variable according to the evaluation result. , or the selection section 5 is a section that determines the target value B*, which is the evaluation standard, according to the current state.

[0009] Generally, a fuzzy model has the following rule format.

0010

[Math 1]

[0011] Here, f() represents the included function. Also, x
, u indicate the state vector and input vector, respectively, and t
is the current time, and dt is the sampling period.

[0012] Also,

[0013]

[Math 2]

##EQU1## where A and B are state spaces, and U is a label of a reference fuzzy set defined on the input space. NA, NB, and NU each represent the number of defined reference fuzzy sets.

[0015] The rules for state predictive fuzzy control are as follows:

0
016]

[Math 3]

It is given by: Here, B* j is a reference fuzzy number representing a target on the state space, and u ′|t is a temporary manipulated variable. Uj can be anything as long as it is given as a temporary manipulated variable, and u|t in the conclusion part is the same Uj
All you have to do is become

However, when trying to control a dynamic system, the state predictive fuzzy control method has the following two problems.

The first problem is that the model calculates predicted values when given each temporary operation amount from the past and present state of the system, and then evaluates each predicted value using the target evaluation rule. Since the controller has a two-stage structure, the inference speed is slow.

The second problem is that since fuzzy inference is divided into two stages: model and target evaluation, it becomes difficult to design and difficult to handle when used simultaneously with normal feedback type fuzzy control rules.

[0021]

[Problems to be Solved by the Invention] As mentioned above, state predictive fuzzy control methods have slow inference speeds and are difficult to design and difficult to handle when used simultaneously with normal feedback fuzzy control rules. There was a problem.

The present invention was devised to solve these problems, and provides a fuzzy control method that can significantly reduce inference time and facilitate simultaneous use with feedback-type fuzzy control rules. The purpose is to provide

[Configuration of the invention]

[0024]

[Means for Solving the Problems] In order to achieve the above-mentioned object, the fuzzy control method of the present invention is based on past manipulated variable inputs to a controlled system and observed values of past and current system state quantities. Using a fuzzy model, the predicted state of the system is determined separately for each provisional manipulated variable, and the degree of conformity of each predicted value to the control target value is evaluated using fuzzy rules for objective evaluation. This is a fuzzy control method that determines the optimal manipulated variable using - It is characterized by grouping each rule and setting a temporary operation amount in advance.

[0025]

[Operation] In the fuzzy control method of the present invention, when a fuzzy model is used in state prediction type fuzzy inference, only a small number of rules among all the rules are used in the fuzzy model to predict one provisional manipulated variable. By taking advantage of the property that the fuzzy model is a single part, the rules of the fuzzy model and the evaluation rules are grouped for each temporary operation amount, and the temporary operation amount is set in advance, thereby shortening the inference time. It also facilitates simultaneous use with feedback-type fuzzy control rules.

[0026]

Embodiments Hereinafter, embodiments of the present invention will be described with reference to the drawings.

FIG. 1 is a block diagram for explaining a fuzzy control method according to an embodiment of the present invention.

In the rule shown in Equation 3 of the conventional fuzzy control method, the restrictions of Equations 4 and 5 are added to the temporary operation amount u ′ given to the fuzzy model, and the temporary operation amount is equal to the amount of operation used in the antecedent part of the fuzzy model. Assume that we use the same reference fuzzy sets and that they are disjoint. Then, the fuzzy model rules and the target evaluation rules (state prediction type fuzzy control rules) can be easily grouped. Condition 5 is automatically satisfied when the temporary manipulated variable is a non-fuzzy value.

[0029]

[Math 4]

[0030]

[Math 5]

That is, when Ui is added to the fuzzy model, fuzzy model rules that do not have the proposition (u|t = Ui) in the antecedent part have a fitness of 0 and are not used. A rule with (u|t = Ui) can also be considered excluding that proposition since its fitness is 1.0. By utilizing this fact, it is possible to extract and group only the rules related to each provisional operation amount from among the rules of the fuzzy model. As an example, consider the following fuzzy model.

[0032]

[Math 6]

Here, from condition number 3

[0034]

[Math 7]

When set as follows, the following two state prediction type fuzzy control rules exist.

[0036]

[Math. 8]

Considering the fuzzy model rules related to RR1, R3 and R4, which do not have (u'|t = UA) as a condition, can be ignored, and for R1 and R2, (u'|t = UA) can be ignored. It can be deleted and associated as shown below.

<Group of rules M |u′=UA when UA is used as the temporary operation amount u′|t>

00
39]

[Math. 9]

Even if u'=UB is considered, a similar rule group M|u'=UB can be obtained. These rule groups do not interfere with each other at all, and the values to be given as temporary manipulated variables are given in advance, so if you give the current state quantity, past state quantity, and manipulated quantity to the model room part, it will work as it is. The ratio at which (u|t = UA) should be output is determined. The above is exactly the same in the case of a fuzzy model rule whose conclusion part is a mathematical formula. In other words, one rule group can be handled in the same way as one conventional state feedback type fuzzy control rule. This allows the feedback type fuzzy control rule and the synthesis/selection section 4 in FIG. 2 to be shared, facilitating simultaneous use.

Furthermore, since the number of model rules used for each temporary operation amount is minimized, the inference speed is greatly improved.

[0042]

[Effects of the Invention] According to the present invention, fuzzy model rules
By grouping the rules and target evaluation rules for each temporary operation amount and setting the temporary operation amount in advance, the inference time can be greatly reduced and the feedback type fuzzy control rule can be
It also makes it easy to use it simultaneously with other tools.

[Brief explanation of the drawing]

FIG. 1 is a block diagram for explaining a fuzzy control method according to an embodiment of the present invention.

FIG. 2 is a block diagram for explaining a conventional state prediction type fuzzy control method.

Claims (1)

[Claims] [Claim 1] Using a fuzzy model, a predicted state value of the system is obtained separately for each provisional manipulated variable from past manipulated variable inputs to the controlled system and observed values of past and present system state variables, This is a fuzzy control method that uses fuzzy rules for objective evaluation to evaluate the degree of compatibility between each predicted value and the control target value, and uses this degree of compatibility to determine the optimal manipulated variable. The present invention is characterized in that, for each given provisional operation amount, rules of the fuzzy model are grouped by related rules, and the provisional operation amount is set in advance. Fuzzy control method.