Proceedings of the Fifth International Conference on Machine Learning and Cybernetics, Dalian, 13-16 August 2006

1-4244-0060-0/06/$20.00 ©2006 IEEE 850

LEARNING AND CONTROL OF HUMAN-LIKE THINKING PROCESS

PEI-JIN WANG

School of Computer Science and Engineering, Yantai University, Yantai 264005, China E-MAIL: wpj@ytu.edu.cn

Abstract: Human thinking is probably divided into three kinds:

iconic imagery thinking, abstract logical thinking and afflatus thinking, but human control thinking process maybe classified as two kinds which are active control thinking process and passive control thinking process. The control methods and strategy of each control thinking process are discussed in this paper, The test results are given in this paper, it is proved that the research content is right and could be used in the process control. It provides the basis for Human-like intelligent control to be used in complex system.

Keywords: Learning; Intelligent Control; Human-like Intelligent

Control; Human thinking

1. Introduction

Complex system has some specific characteristics such as time delays, large time constants, uncertainties, nonlinearities, unmodelled, time change, dynamics, etc. In order to solve the problem of complex system control, much more attention is being paid on artificial intelligence (AI) techniques with three methods: expert system, neural network and fuzzy control, so there are a lot of papers discussing the above methods. But the above methods actually learn from the human intelligence, human intelligence is the basis to all of intelligent control methods. Any complex system could be controlled by human, the main problems in the area of human-like intelligent control are that how to rediscover human control experience and methods, how to simulate human control thought process by computer and how to design human-like intelligent controller. The Model of Human-like intelligent inference and control is presented in the previous paper [1] .

Human thinking is probably divided into three kinds: iconic imagery thinking, abstract logical thinking and afflatus thinking. Iconic imagery thinking and abstract logical thinking are the basic thinking, afflatus thinking is produced by the two thinking mixture. So human control thought process also includes the above three thinking, according to the research, iconic imagery inference control

thinking and abstract logical inference control thinking could be simulated by computer, it is impossible to simulate the afflatus inference control thinking now .

There are some differences between human general thinking and human control thinking. Some ideas, results, conclusions and deductions come from human general thinking, but control actions with quantities adjustment are generated by the human control thinking. If a control action is generated according to iconic imagery information and past control experiences of the controlled system, it is called iconic imagery inference control; if a control action is generated according to some definite calculations based on the mathematical relationships between output parameter and input parameter, it is called abstract logical inference control. There is no restrict mathematical relationship between input and output of iconic imagery inference control, but there is a restrict mathematical relationship between input and output of abstract logical inference control. Abstract logical inference control play an important role at the first stage of human control process, with the collection of control experiences and control workmanships, iconic imagery inference control will plan an important role later. So iconic imagery inference control is regarded as more advanced than abstract logical inference control in human control thinking process, but abstract logical thinking is regarded as more advanced than iconic imagery thinking in general human thinking process.

On the other hand, human control thinking process could be divided into two kinds which are active control thinking and passive control thinking process according to a human operator control process. Abstract logical inference control play a main role in active control thinking process, and control experiences are also taken place in the process; Iconic imagery inference control play a main role in passive control thinking process, control strategy is generated and used in the process.

In this paper based on the single input single output system, a real structure of human-like intelligent inference controller which finds an application in the practical control system is pointed out and analyzed. The general structure of the controller is described in section 2. Analysis of human

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control thinking process is discussed in section 3. Control methods and strategy are described in section 4. Finally, some conclusions are given in section 6.

2. Structure of the controller

The detail structure of the controller is described in reference [3]. Some parts of the controller is introduced as the following.

2.1. Iconic Imagery Inference Controller

The inference process of iconic imagery inference controller is described in figure 1 .

Figure 1. Inference process of the controller

It is known from the figure 1 that input of the controller is qualitative information, output of the controller is quantitative information. The detail inference process of the iconic imagery control is that feature number of control rule (FNR) is first calculated according the present E and EC; second, the control rule is looked up according to the FNR from the rule database; third, if only one control rule is looked up, then the value of manipulated variable in the rule is outputed; if more than one rules are looked up, the average value of manipulated variable of the rules are calculated and outputed.

2.2. Abstract Logical Inference Controller

Beginning with no rules in the rule database or with few rules from the human-machine interface, PID controller or other kind of controllers is chosen as the first controller which is called abstract logical inference controller in the beginning stage. Because output of the abstract logical inference controller is calculated according to the controlling method or algorithm, it is different from the iconic imagery inference controller. If PID controller is used, all three PID gains are tuned initially using conventional tuning methods.

2.3. Open-Closed Loop Control Switch

Open-Closed loop control switch is one of important

characteristics of human-like intelligent controller[3]. It is shown in Figure 2.

Figure 2. Open-Closed Loop Control Switch

Open-Closed loop control switch is also one of the

methods keeping the control system stable. In the open loop control process, there is no output of the controller, but the controller continue to sample data, doing some analysis. Three dynamic feature variables are defined to be used to identify open-closed loop control switch [3].

3. Analysis of human control thinking process

Human control thinking process could be divided into two kinds which are active control thinking and passive control thinking process according to a human operator control process. The active control thinking process is described as the following case: based on the desired control goal and performance, a human operator should do some control actions actively at the first stage of the whole control process. A initial value of manipulated variable is first calculated according to the static relationship between the controlled variable and manipulated variable, and is outputted to make the controlled variable change fast from zero or a initial value to the desired value. At the time, the operator can take such strategy as “ adjusting then observing then waiting then re-adjusting ” until the controlled variable arrives to the desired value and keeps itself relatively stable.

The passive control thinking process occurs when the controlled variable leave away the desired value because of some disturbances. When the controlled variable is off the desired value, the operator is forced to do some control actions and try to keep the output stable, so the adjusting process is passive, the operator should not do any control action if the controlled system was in stable state.

Control experiences are generated during the active control thinking process. According to the current changing tendency of the controlled variable, the operator adjusts the actuator actively and continuously, each operation is stored in the operator brain, specially the current input, output, control result, control validation are saved in the brain. The above information is also called control experiences, so

Qualitative information ofthe controlledsystem

Iconic imagery inference controller

Value of Manipulated Quantitativeinformation

Observation State analysis

Stable Open loop control

Non-stable Close loop control

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control experiences are collected and processed in active control thinking process.

Control experiences are modified and simplified to be control strategy which is used as iconic imagery inference control output in passive control thinking process. The relationships among active control thinking process, passive control thinking process, control experiences, control strategy are shown in Figure 3.

Figure 3. The relationships

4. Control methods and experiences

In this section how to simulate a human operator active control thinking process by a computer is discussed.

The positive change direction relationship between controlled variable and manipulated variable is defined as when the output of manipulated variable is greater, the controlled variable change is also greater; on the otherwise, when the output of manipulated variable is smaller, the controlled variable change is greater, it is defined negative change direction relationship. Suppose the relationship is positive change direction relationship, a set value, changing range and max-min value of controlled variable Y is given, and adjusting range of manipulated variable U is also given. According to the Figure 4, three regions and five states are described in Table 1.

Figure 4. Controlled variable change Y(t)

Table 1. Regions Negative Zero Positive States Negative

over the certain limit

Negative non-stability in the certain limit

Stability in the certain limit

Positive non-stability in the certain limit

Positive over the certain limit

Range 0 , Y-2 Y-2 , Y-1 Y-1 , Y+1 Y+1 , Y+2 Y+2 , ∽where：e = Y- Y0 ，set error ε，max set error εmax then：Y+1 = Y0 +ε， Y-1 = Y0 –ε，Y+2 = Y0 +εmax , Y-2 = Y0 -εmax

4.1. Control methods

4.1.1. Considering a initial value of manipulated variable

The first important thing to a human operator is how to get a initial value of manipulated variable , to start control process, and to make the controlled variable change fast to the set value. The static relationship could be established by a test, there exists a nonlinear and non-single value relationship between manipulated variable and controlled variable which is shown in Figure 5.

The above relationship could be represented by a

neural network, if Y0 is given, U0 is calculated by the neural network and outputted as the initial value.

4.1.2. Next step control methods

When the first output U0 is outputted, the controlled variable will change from zero to the set value. At the time open-loop control is taken, a computer samples current change Yi, if Yi gets into the negative non-stable state in table 1, closed-loop control is switched as the following steps: (1) The first time closed-loop control (n=1)

U1 = U0 + ∆u if Y1 is in negative region U1 = U0 –∆u if Y1 is in positive region U1 = U0 if Y1 is in zero region

(2) Beginning from the second time closed-loop control, output Un is calculated as the following formula: Un = Un-1 + ∆u, if Yn-1, Yn-2 is in negative region Un = Un-1 –∆u if Yn-1, Yn-2 is in positive region Un = (Un-1 + Un-2)/2, if Yn-1 is in negative region and Yn-2 is in positive region, or if Yn-1 is in positive region and Yn-2 is in negative region.

Initial control experiences Modified experiences strategy

Active control thinking process Passive control thinking process

Y(t) Y+2 Y+1 Y0 Y-1 Y-2

U

Y

Ui

Yi

Yi

Figure5. U-Y relationship

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Un = Un-1 if Yn-1 is in zero region Where: ∆u = 1/（Umax – Umin）

4.1.3. Control effect validation

It is important how to evaluate closed-loop control effect after the output of manipulated variable. If control action is effective, observing the change and waiting a time; if control action is uneffective, the closed-loop control is going on until the controlled variable is in relatively stable state. The following criteria is used to evaluate the closed-loop control effect: (1) The error decreases after the output of manipulated variable |ei+1| < |ei| (2) The changing tendency is opposite from ∆ei >0 to ∆ei+1 <0，or ∆ei <0 to ∆ei+1 >0 (3) The controlled variable is going forward stable |∆ei| = |yi+1 – yi |= |∆yi| ≈0 (4) The condition from closed-loop control to open-loop control is defined as the following: |e| ≤ ε，|∆e| ≈0 ，or e = 0.

4.2. Control experiences

The advantage of computer control is great data processing ability and information storage. All of the information such as previous adjusting value of manipulated control Ui-1 , previous adjusting value of controlled variable Yi-1 , current value of manipulated control Ui , current value of controlled variable Yi , control effect, adjusting effect, sampling time and system stability are stored as initial control experiences. Control experience and rule mining unit will process the initial control experiences.

Control strategy will be generated in passive control thinking process.

4.2.1. Initial data processing

A lot of control data is stored as the active control thinking process is going on, some unuseful data will be deleted according to the following rules:

(1) if Yi+1 > Y0 +εmax

(2) if Yi+1 < Y0 - εmax

(3) Control result is uneffective The current value of controlled variable Yi, the

changing value of controlled variable ∆Yi, The current value of manipulated variable Ui, the changing value of manipulated variable ∆Ui, the current error Ei, and the current state of controlled variable Si (NB, NS, ZO, PS, PB) are defined and calculated from the left stored data, the above variables are representative of the control

experiences. According to the control experiences, the passive

control thinking process is realized in Figure 6.

Figure 6. passive control thinking process Ui+1 = Ui + ∆Ui

4.2.2. Modify the control experiences in passive control thinking process

The passive control thinking process is the opposite control process of active control thinking process. Suppose that the relationship between controlled variable and manipulated variable is positive relationship, when a positive output ∆U will lead to a positive change ∆Y in active control thinking process; when a positive change ∆Y takes place because of some disturbance, in order to recover the controlled variable change, a negative output –∆U should be outputted, and then a negative change –∆Y is generated to cancel the previous change ∆Y . This is a ideal casino. Actually, there exists no-single and no-linear relationship between them. So when a negative output –∆U is outputted, and then a negative change –∆Y/ is generated, but |∆Y|﹦|–∆Y/|. If |∆Y|-|–∆Y/| < ß, the control result is effective.

The passive control thinking process is simulated by a computer as the following steps: (1) the plant is in open-loop state, the controlled variable is relatively stable, the current manipulated variable value is U0, sampling the current controlled variable Yi ; (2) calculate Current error : Ei = Yi – Y0; (3) if | Ei | < ε, go to step 1; or go to next step; (4) ∆Yi=Yi – Yi+1 , Ui and ∆Ui are calculated according to the Figure 6. Ui+1 = Ui +∆Ui Ui+1 is outputted .

(5) waiting for a short time; (6) sampling the current value Yi, if |∆Y|-|–∆Y/| < ß and |Ei+1|< ε, the control result is effective, go to step 1; or it is uneffective, go to the next step; (7) chose a small value δu,

if |∆Y| < |–∆Y/|, Ui+2= Ui+1-δu

Inference Network

Ei

Yi

∆Yi

∆Ui

Ui

Si

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if |∆Y| > |–∆Y/|, Ui+2= Ui+1+δu Ui+2 is outputted.

(8) waiting a short time, if | Ei+2 | >ε, go to step 7; or go to next step; (9) modify control experiences, go to step 1.

5. A case study

The structure of the human-like intelligent inference and control and the above control methods are simulated by using Dynamic Data Exchange technology with Delphi program language. DDE means data exchange among windows application programs dynamically. One is called server which provides data to the other, one is called client which accepts data from a server. One application program can be both a server and a client. Delphi is a better program language for developing computer control software.

The implementation result for no disturbance is shown in Figure 7, The implementation result for disturbance is shown in Figure 8.

Figure 7. Implementation result for no disturbance

So a good test result is implemented, it is proved that

all above discussion is right.

6. Conclusions

All of intelligent control methods such as fuzzy control, neural control, expert control, etc learn from human control experiences and methods, human-like intelligent control is the basis of other intelligent control. Now there

Figure 8. Implementation result for disturbance

are a few papers discussing human-like intelligent control. It is impossible to simulate human-like intelligent control completely by using present computer technologies, but human iconic imagery control thinking and abstract logical control thinking has been proved to be implemented in this paper. The structure of the human-like intelligent inference and control has some advantages with simulating human control thought process nearly, making open-close loop control switch into reality, identifying control stage, mining control experiences and extracting control rules in on-line control process. The feature of the controller is intelligent, realizable and stable. The controller is also used to control the water level of a water level control test system, the better control result is gotten.

References

[1] Implementation of Human Like Intelligent Inference and Control，the International conference on intelligent control, 2003，USA .

[2] Study on the Human Like Intelligent Inference and Control, The second International Conference on Machine Learning and Cybernetics , IEEE System, Man and Cybernetics Technical Committee on Cybernetics, 2003 Xian, China.

[3] Peijin Wang, Study on the Model of the Imitation of Human Intelligent Deduction and Control , J. Of Computer Engineering & application, Vol 37(14) , Chinese Institute of Computer, 2001.

[4] Peijin Wang, Intelligent Control of the Complex System, J. Of Computer Engineering & application, Vol.36(7) Chinese Institute of Computer, 2000.

[5] Peijin Wang , Study on the Stability of the Imitation of Human Intelligent Control , J. Of Computer Engineering & application, Vol. 37( 6), Chinese Institute of Computer, 2001.

[6] Peijin Wang, Chris Cox, Study On the Application of Auto-Associative Neural Network in Sensor Validation for Water Treatment Plant, The 4th Asian Control Conference , Singapore ,2002,09.

[7] Peijin Wang, PI-Tuning Methods Based on GA, The first International Conference on Machine Learning and Cybernetics , IEEE System, Man and Cybernetics Technical Committee on Cybernetics, Beijing, China, 2002,11

[8] Dr. Peijin Wang received B.Sc. Engineering, M.Sc. Computer application, Ph.D. Computer application. He is currently a professor at Yantai University, Yantai City, Shandong Province , China. As a visiting scholar he studied in University of Sunderland ,UK for half year. His main research interests include Artificial Intelligence, Computer Control, Intelligent Control and Human_Like Intelligent Control.