**3. Expert control and humanoid intelligent control**

#### **3.1 Expert control**

An expert is someone who has deep theoretical knowledge or rich practical experience in a certain field. Experts' decision-making actions to solve difficult problems can achieve important results because they have accumulated valuable theoretical knowledge and practical experience in their heads. We may use some kind of knowledge acquisition method to store expert knowledge and experience in the professional field into the computer, and rely on its reasoning program to make the computer work close to the level of the expert. It can be based on the special domain knowledge and knowledge provided by one or more human experts. Use experience to reason and judge. An expert system is a computer program system with a large amount of expertise and experience [10].

The basic structure of an expert system usually consists of five parts: a knowledge base, a database, an inference engine, and an interpretation part and knowledge acquisition.

In terms of the main features and structure of the expert system, the industrial production process places several special requirements on the expert control system that are different from general expert systems, including (1) high reliability and long-term continuous operation. (2) Real-time nature of online control. (3) Excellent control performance and anti-interference. (4) Flexible and easy to maintain [11].

Because industrial process control has the aforementioned special requirements for expert control systems, expert control systems control process objects, and domain expert knowledge is usually represented by production rules. Generally speaking, the expert control system consists of the following parts: (1) Database. (2) Rule base. (3) Inference engine. (4) Human-machine interface. (5) Planning.

Constructing an expert control system requires not only complex design and long commissioning cycles, but also a large amount of human, material and financial resources. Therefore, for some controlled objects, considering the control performance indicators, reliability, real-time performance, and performance/price ratio requirements, the expert control system can be simplified.

Expert controller is usually composed of four parts: knowledge base, control rule set, reasoning mechanism and information acquisition and processing. The scale of the knowledge base and control rule base of the expert controller is small, and the reasoning mechanism is simple. Therefore, it can be controlled by microcontroller, programmable controller (PLC), etc. to realize.

According to the characteristics of industrial process control, production rules are used to describe the causality of the controlled process, and control rule sets can be established through fuzzy control rules with adjustment factor analysis and description [12].

Set the input set E and output set U of the expert controller to be Eq. (13) and (14)

$$E = \{ \begin{array}{c} -e\_n, -e\_{n-1}, \dots, -e\_1, 0, e\_1, e\_2, \dots, e\_n \end{array} \} \tag{13}$$

$$U = \{-u\_n, -u\_{n-1}, \dots, -u\_1, 0, u\_1, u\_2, \dots, u\_n\}$$

$$f(E) = U\tag{14}$$

*Overview of Some Intelligent Control Structures and Dedicated Algorithms DOI: http://dx.doi.org/10.5772/intechopen.91966*

The control rule set is summarized and summarized based on the knowledge set. It reflects the expertise and experience of experts, and reflects the intelligent control decision-making behavior of people in the operation process. The design control rule set includes the following 6 rules:

1. IF *E* > *EPB* THEN U=*UNB.*

2. IF *E* < *ENB* THEN U=*UPB.*

3. IF *C* > *CPB* THEN U=*UNB.*

4. IF *C* < *CNB* THEN U=*UPB.*

5. IF *E*•*C* < 0 OR *E* = 0 THEN U=INT[*αE*+(1-*α*)*C*].

6. IF E�C > 0 OR *C* = 0 AND *E* 6¼ 0 THEN *U* = INT[*βE*+(1-*β*)*C*+*γ* P*<sup>k</sup> <sup>i</sup>*¼<sup>1</sup>*Ei*].

Among them, *E, C*, and *U* are fuzzy variables of error, error change, and control amount, respectively, and the quantization level of *C* is selected exactly the same as *E, U*; and the maximum positive values of *E, C*, and *U,* respectively, and *ENB*, *CNB* and *UNB* are negative maximums of *E, C,* and *U*, respectively; *α, β*, and *γ* factors to be adjusted are determined by empirical rules of knowledge concentration; P*<sup>k</sup> <sup>i</sup>*¼<sup>1</sup>*Ei* intelligent integration terms for errors are used to improve the stability of the control system State performance; the symbol INT〔a〕means to take an integer closest to *a.*

Considering that the control decision of the expert controller completely depends on the characteristics of the input data, the controller adopts a data-driven forward reasoning method to sequentially determine the conditions of each rule. If the conditions are met, the rule is executed, otherwise the search is continued. Since there are corresponding controls rules for each of the control input variables £: and C, the target can be searched.

Simulation and practical application show that the above-mentioned expert controller not only has the characteristics of fast dynamic response, small overshoot, and high steady-state accuracy, but also has simple control algorithm programming, flexible control rule modification, good real-time performance, and changes in the parameters of the controlled object. Has strong robustness.

#### **3.2 Human-like intelligent control**

Conventional PID control controls the controlled object based on the linear combination of the proportional, integral, and derivative of the controlled system error. According to the mathematical models of different control objects, the three control parameters Kp, Ki, and Kd of the PID are appropriately set to obtain a satisfactory control effect. Linear PID control cannot solve the problem that increasing the control amount can reduce the steady-state error and improve the accuracy, but it will reduce the stability. Physically speaking, the control process is the process of information processing and energy transfer. Therefore, the information processing ability is improved, a more reasonable control law is designed, and the energy transmission of the controlled system is achieved too quickly, stably, and accurately in the shortest time and/or the lowest cost. This is the key problem to be solved in the control system design [13].

It is not enough to use only linear control methods in PID control. It is necessary to introduce some non-linear control methods as needed. The system's dynamic

process and transient process, according to the needs of the system's dynamic characteristics, behavior and control performance, use variable gain (gain adaptive), intelligent integration (non-linear integration) and intelligent sampling. This requires expert control experience, heuristics Intuitive judgment and intuitive reasoning rules. Such control decisions are conducive to solving the contradiction between fastness, stability and accuracy in the control system, and can enhance the adaptability and robustness of the system to uncertain factors.

Intelligent control basically imitates human intelligent behavior for control and decision-making. Some scholars have found through experiments that after obtaining the necessary operational training, the artificially implemented control method is close to optimal. This method does not require knowledge of the structural parameters of the object, nor does it require the guidance of an optimal control expert. In the following analysis of the step response characteristics of the secondorder system, we can see the basic idea of implementing human-like intelligent control (see **Figure 19**) [14].

It can be found in **Figure 14** (1) that variable gain control should be used in the OA segment. Use a larger gain in the initial section and increase it to a certain stage to reduce the gain so that the system continues to run through the inertia rise. (2) In section AB, the control function shall try its best to reduce the overshoot. In addition to proportional control, the integral control function should be added to enhance the control function through the integral error and make the system output return to the steady state value as soon as possible. (3) In the BC segment, the error starts to decrease, and the system shows a steady state change trend under control. At this time, no integral control operation should be added. (4) The system output decreases in the CD segment, the error changes in the opposite direction, and reaches the maximum value (positive) at point D. At this time, proportional plus integral control should be used. (5) In the DE segment, the system error gradually decreases, and the control effect should not be too strong, otherwise overshoot will occur again.

The basic idea of human-like intelligent control is to use computer to simulate the artificial control behavior in the control process, to maximize the identification and use of the characteristic information provided by the dynamic process of the control system, to make heuristic judgment and intuitive reasoning. This can effectively control objects that lack accurate models.

#### *3.2.1 Characteristic variables of system dynamic behavior*

In order to use a computer to automatically realize human-like intelligent control, the system must be able to automatically recognize the dynamic behavior of the control system through some characteristic variables in order to mimic human intelligent control decision-making behavior. In fuzzy control, the error *e* and the

**Figure 19.** *Unit step response curve for second-order system.*

*Overview of Some Intelligent Control Structures and Dedicated Algorithms DOI: http://dx.doi.org/10.5772/intechopen.91966*

error change Δ*e* are usually selected as the input variables of the fuzzy controller. Generally, the output u of the fuzzy controller can be expressed as Eq. (15).

$$
\mu = f(e, \Delta e) \tag{15}
$$

If the control is based on the magnitude of the error *e*, it is difficult to obtain satisfactory control results for some complex systems. For example, when the controlled system has a large error and it is changing rapidly in the direction of reducing the error, if only based on the large error and not taking into account the rapid change of the error, it is necessary to increase the control amount so that the system eliminates large errors as soon as possible. Error, such control will inevitably lead to the negative consequences of over-regulation and reverse error. When two input variables *e* and Δ*e* are used for control, the above-mentioned blindness can be avoided. Therefore, for a complex system under manual control, the more people learn about the state, dynamic characteristics, and behavior of the controlled system during the control process, the better the control effect will be.

How to identify the state, dynamic characteristics and behavior of the controlled system according to the input and output information is the first problem to be solved by human-like intelligent control. To this end, starting from the two basic variables of error *e* and error change Δ*e*, a characteristic variable is designed to identify the characteristic mode of the dynamic process.
