*3.2.1.5 Characteristic variable Δ (*Δe*)*

The sign of the change rate (secondary difference) of the error change is defined as a characteristic quantity that describes the dynamic process as overshoot or callback. For example, for the curve shown in **Figure 19**, there are two cases:

1.ABC segment: Δ(*Δe*) > 0, it is in the overshoot segment.

2.CDE segment: Δ(*Δe*) < 0, which is in the callback segment.

The essential characteristics of the above-designed characteristic variables are that they are not an absolute quantity, but a symbol variable, or a relative quantity. Symbol variables are used to characterize the direction of the dynamic process change trend, and relative quantities are used to characterize the speed of the dynamic process change. The above-mentioned symbol variables and characteristic variables (relative quantities) that characterize the degree of change in a dynamic process are collectively referred to as qualitative variables.

In order to use computers to realize human-like intelligent control, it is necessary to try to teach human operation experience, qualitative knowledge and intuitive reasoning to the computer, and let it apply this knowledge through flexible and flexible judgment, reasoning and control algorithms to perform human-like intelligent control. The main source of online information obtained by a computer is the input *R* and output *Y* of the system, from which the error *e* and the error change *Δe* can be calculated. Through *e* and *Δe*, the characteristic quantities that characterize the dynamic characteristics of the system can be further obtained. The computer can capture the characteristic information of the dynamic process with the aid of the above-mentioned characteristic quantities, and recognize the dynamic behavior of the system as a basis for control decisions. According to the dynamic characteristics and dynamic behavior of the system, the most effective control form is selected from a variety of control modes to precisely control the controlled object. Computers can use qualitative knowledge and intuitive reasoning in the control process. This is fundamentally different from traditional control theories, and it is precisely this point that embodies human intelligence. This method solves the contradiction of speed, stability and accuracy in the control process very well.

#### *3.2.2 Humanoid intelligent control principle*

The composition of human-like intelligent controller is similar to the basic structure of an expert controller, which consists of the following four parts.


4.Control rule set. The control rule set is actually a rule-based controller. The process of control decision is to implement a mapping from the feature pattern set to the control rule set. In general, the number of feature patterns is greater than or equal to the number of control rules.

The working process of the human-like intelligent controller can be summarized into three steps: First, the system judges the characteristic mode of the dynamic process according to the calculated characteristic variables; second, the inference mechanism searches for a matching control rule according to the characteristic mode class; third, the controller executes the above control rules to control the controlled object [15].

This completes a step-by-step intelligent control algorithm, and then cyclically controls step by step until the error of the controlled system reaches the desired index.

#### **3.3 Multiple modes of human-like intelligent control**

A variety of human-like intelligent control modes have been formed to imitate human control and decision-making processes: humanoid intelligent switch control, humanoid proportional control, humanoid intelligent integral control, humanoid intelligent sampling control, and humanoid extreme value sampling and control. In addition, in the human-like intelligent control, a combination of variable gain proportional control, proportional differential control, and open-loop and closedloop control is also used.

#### *3.3.1 Human intelligence integration principle*

#### *3.3.1.1 Human-like intelligent integration principle*

The introduction of integral control in the control system is an important way to reduce the steady-state error of the system. **Figure 21(c)** shows the integral process of the integral control action on the error in conventional PID control. This integral effect simulates human memory characteristics to a certain extent. It "remembers" all the information about the existence and changes of errors. The disadvantages of the integral control function based on this integral form are: first, the integral control function is not targeted, and sometimes does not meet the objective needs of the control system; second, because this integral effect is always integrated as long as the error exists, it is easy to cause integral saturation in the application of actual practice, which will reduce the rapidity of the system. Third, the integral parameters of this integral control are not easy to select, and improper selection will cause the system to oscillate.

The reason why the integral control function is not good is that the integral control function does not well reflect the intelligent control decision-making thoughts of experienced operators. In the integral curve interval (a, 6) in **Figure 21(c)**, the integral effect is opposite to the control effect of an experienced operator. At this time, the system has overshoot. The correct control strategy should be to add a negative control value to the constant value to reduce the overshoot and reduce the error as soon as possible. However, the integral control effect in this interval increases a positive control amount. This is because the integral result in the (0, *α*) interval is difficult to be offset and the sign is changed, so the integral control amount remains positive. As a result, the system overshoot cannot be reduced quickly, which prolongs the transition process time of the system.

In the (6, *c*) section of the above integral curve, the system error changes from a maximum value to a decreasing direction, and there is a trend of steady state change. At this time, a certain proportional control effect should be added, but the integral control effect should not be added. Otherwise, it will cause system callback. *Overview of Some Intelligent Control Structures and Dedicated Algorithms DOI: http://dx.doi.org/10.5772/intechopen.91966*

#### **Figure 21.**

*Error and error integration curve. (a) (y (t)) Unit step response curve for second-order system. (b) (e (t)) Unit step response curve for second-order system. (c) In conventional PID control Integral control is the process of integrating errors. (D) Integrate on equal intervals.*

In order to overcome the shortcomings of the integral control function described above, the integral curve shown in **Figure 21(d)** is used, that is, the integration is performed in the intervals (a, b), (c, d), and (e, f). The integral can provide the correct additional control amount for the integral control function in a timely manner, and can effectively suppress the increase of system error; while in the interval (0, a), (b, c), and (d, e), stop integral role to facilitate the system to transition to a steady state by virtue of inertia. At this time, the system is not in a state of out of control, it is also restricted by control functions such as proportion.

This integral function better simulates human memory characteristics and human-like intelligent control strategies. It selectively "remembers" useful information and "forgets" useless information, so it can overcome the shortcomings of general integral control. It has the characteristics of non-linear integration of human-like intelligence, which is called such integration of human-like intelligence.

#### *3.3.1.2 Human-like intelligent integration control algorithm*

In order to introduce the function of intelligent integration into the control algorithm, we must first solve the problem of logical judgment of introducing intelligent integration.

**Figure 22.** *Structure of a humanoid intelligent controller.*

This condition can be determined by comparing the intelligent integration curve in **Figure 21** with **Figure 22** and **Table 2** [16].

When the error *en* and the error change *Δen* at the current sampling time have the same sign, that is, *en•Δen >* 0, the error is integrated; on the contrary, the error *en* and the error change *Δen* have different signs, that is, when *en•Δen* < 0, errors are not integrated. This is the basic condition for introducing intelligent integration. Considering the extreme points of errors and error changes, that is, the boundary conditions, the conditions for introducing intelligent integration and not introducing intelligent integration can be synthesized as follows:

When *e•Δe* > 0 or *Δe* = 0 *and e* 6¼ 0, the error is integrated, that is, intelligent integration; when *e•Δe* < 0 *or e =* 0, the error is not integrated, that is, no integration effect is introduced.

The digital simulation results show that the human-integrated intelligent integral control algorithm significantly improves the steady-state accuracy of the fuzzy control system due to the introduction of intelligent integral control. Compared with ordinary fuzzy controllers, the human-integrated intelligent integral control algorithm has the advantage of high steady-state accuracy. Compared with conventional PID control, this control algorithm has the advantages of fast response speed, small overshoot, or no overshoot. Therefore, this is a control algorithm with simple structure and good control performance for intelligent control.

#### *3.3.2 Multiple modes of human-like intelligent control*

The computer control systems of most production processes are continuous discrete hybrid systems. In such a system, the detection of time-continuous signals and the output of computer-controlled quantities, considering the problem of signal reproduction, require the correct selection of discrete-time sampling periods. In the control process, the main consideration is to help improve the control quality as much as possible.

#### *3.3.2.1 Effect of sampling period on digital control*

The upper limit of the sampling period is selected, but the choice of the lower limit of the sampling period is restricted by many factors. The smaller the sampling

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

period is better to reproduce the signal. However, if the sampling period is too small, the signal-to-noise ratio is low, the quantization noise is large, and it is easy to be interfered, which affects the control performance. In process control, the lower limit of the control cycle selection is limited by the control algorithm operation time. Therefore, the sampling period and the control period are not both as small as possible.

As for the analog design method of the digital controller, the smaller the sampling period, the closer the characteristics of the digital controller are to the characteristics of the analog controller. As for the discrete design methods of digital controllers, most are based on the discretized object model. The discretization model of the object depends on the selection of the sampling period, so the sampling period not only affects the distribution of the zero and pole positions of the model, but also affects the accuracy of the model. Too long a sampling period may even lead to the loss of useful high-frequency information, thereby reducing the model order.

#### *3.3.2.2 Human-like intelligent sampling control for lag process*

A large number of controlled processes have varying degrees of time lag, which brings difficulties to the process system. The ratio of the lag time to the capacity lag time constant *T* reflects the difficulty of control. As the *τ/T* value increases, the difficulty of control increases accordingly. When approaching or exceeding *T*, the effect of using ordinary PID control is very poor, and Smith predictive control must be used. However, Smith control requires an accurate controlled process model, and complex controlled processes are often difficult to establish accurate mathematical models. Therefore, many improvements have been made to Smith control, and some control algorithms have emerged to overcome lag. Nevertheless, it should be said that the problem of large lag process control is still a topic of great concern in the control field.

As we all know, for an object with a pure lag time *τ*, its control effect must be reflected in time *τ*. Therefore, control within time is of no value, so the sampling control shown in **Figure 23** is generated. The sampling period *T*<sup>s</sup> is slightly larger than *τ*, and the control time (on time) *Δt* is about *1*/*10 T*s. This choice will bring two disadvantages: first, the interference and sampling will be seriously out of sync. Because the pure lag time of the controlled process is generally large,*T*<sup>s</sup> is chosen to be large, and the control time *Δt* is very small. In this way, the system is in an openloop state during the *T*<sup>s</sup> *Δt* time of each sampling cycle, and some urgent needs cannot be obtained. Useful information such as changes in output y(*t*) caused by fixed-value disturbances or a given input that requires y(*t*) to track as quickly as possible. Second, the feedback information obtained is too small and untargeted, which makes the control in a blind state, resulting in a long transition process.

The disadvantages of the above sampling control are passive waiting and blind control. In short, such control lacks the intelligent sampling characteristics of the lag

**Figure 23.** *Sampling control principle.*

process of manual control. The basic strategy of manual sampling control to overcome large lags can be described as follows:

Wait ! look ! tune ! wait again ! look again ! adjust again ……

According to the above control strategy, the principle of a human-like intelligent sampling control system is shown in **Figure 24**. Among them, INT indicates intelligent device; *F*(*s*) indicates fixed value interference; *Gcj*(*s*) indicates controller; *<sup>Y</sup>*(*s*) indicates controlled variable; *Gp*ð Þ*<sup>s</sup>* <sup>e</sup>�*τ<sup>s</sup>* indicates controlled object; *<sup>B</sup>* indicates intermediate feedback coefficient; *R*(*s*) indicates interference; *P* takes the constant "1" or "0".

The function of the intelligent device is to control an intelligent sampling switch *K*, which "opens" or "closes" when certain conditions are met, that is shown in Eq. (11),

$$K = \begin{cases} \text{0} \ (\text{Discounted}) & e \cdot \dot{e} < \text{0} \text{ or } |e| < \delta\\ \text{1} \ (\text{Closed}) & e \cdot \dot{e} > \text{0} \text{ or } \dot{e} = \text{0}, |e| \ge \delta \end{cases} \tag{16}$$

In the formula, *e*, *e*\_ are the error and the first derivative of the error; *δ* is the insensitive region.

When the controlled variable deviates from the expected value, the smart device sends a signal that the *K* switch is closed for sampling, and the controller controls in time until the controlled variable has a tendency to return to a balanced position; when the controlled system error value is within the allowable range, switch *K* disconnect, the system is in an open-loop working state. At this time, the energy required to be maintained by the object is supplied by the controller or the stored energy of the object.

In **Figure 24**, *Gcj*(s) represents the *j th* controller *j* ∈ (1,2), which is attractive considering that the given interference and fixed value interference often have different control laws and effects. Its selection is made automatically by the logical relationship of the design like Eq. (9):

$$\mathbf{G}\_{\circ j}(s) = \begin{cases} \mathbf{G}\_{\varepsilon 1}(s), & dc(t)/dt \neq \mathbf{0} \\ \mathbf{G}\_{\varepsilon 2}(s), & dc(t)/dt = \mathbf{0} \end{cases} \tag{17}$$

Among them, *c t*ðÞ¼ *<sup>L</sup>*�<sup>1</sup> <sup>1</sup> *Ts*þ<sup>1</sup> *R s*ð Þ h i,*<sup>T</sup>* is determined as needed. In this way, the adaptability and effectiveness of the controller are enhanced, and it is ensured that

**Figure 24.** *Intelligent sampling control schematic diagram.*

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

**Figure 25.** *Human-like intelligent switch control simulation result.*

the corresponding controller is selected with different interference. The introduction of intermediate proportional feedback *B* is mainly for design convenience.

Through further analysis of the above-mentioned intelligent sampling control mechanism, it can be seen that when the switch *K* is closed and the system is in closed-loop control, whether the system is a follow-up system or a fixed value system, e*<sup>τ</sup><sup>s</sup>* will appear in the characteristic equation, but by setting appropriately due to the logic judgment function of the smart device, the control parameters of the smart device can be established only in a short period of time, so that the unstable factors are eliminated. When the switch is open and the system is in open loop, e*<sup>τ</sup><sup>s</sup>* has no effect on stability. In the above-mentioned intelligent mining control scheme, a smart device is used to determine whether the system is openloop or closed-loop. In the open-loop process, the controller is in the active waiting phase with an observation function. The purpose of this waiting is to prepare for better control. In the closed-loop process, the controller is in the control phase, which is a manifestation of waiting action for strongly targeted. This control method cleverly avoids the adverse effects brought by e*<sup>τ</sup><sup>s</sup>* , and successfully solves the stability problem of the system.

Intelligent sampling control is a novel control method with open loop in the closed loop and closed loop in the open loop. Its entire working process is similar to an experienced operator. It can continuously observe and perform real-time correction as required. Therefore, this control has strong robustness and fastness, and can be easily realized by a microcomputer program.

#### *3.3.3 Programming of human-like intelligent control*

Here is a programming example of human-like intelligent switch control simulation, the simulation results are shown in **Figure 25**, write the MATLAB program as follows:

k = 1;% scaling factor K g1 = tf (1, [8 6 1]);% continuous system model G (s) g2 = feedback (k \* g1,1); tt = 1; y = [0 0.0329];% output matrix initialization u = ones (1,100);% input matrix initialization

u (1) = 0; e = [0 0.9671];% deviation matrix initialization for n = 3: 1: 200 e (n) = k \* (u (n-1) -y (n-1)); if (e (n) -e (n-1) <= 0.0001) & (y (n-1) <= 1)% Judge whether the steadystate and boundary conditions are satisfied tt = tt-1; if tt == 0% judging whether it is within a switching action period u = (u (n) + 0.31 \* e (n)) \* ones (1,200);% Switch action content tt = 40;% set smart switch action period end end e (n) = k \* (u (n-1) -y (n-1)); y (n) = 1.489 \* y (n-1) -0.549 \* y (n-2) + 0.0329 \* e (n-1) + 0.0269 \* e (n-2);% difference equation end t = 0: 1: n-1; g3 = step (g2, t); graphical output of plot (t, y, t, g3)% response curve

Here is a programming example of human-like intelligent integral control simulation, the simulation results are shown in **Figure 26**, write the MATLAB program as follows:

y = [0 0.132];% output matrix initialization u = ones (1,1000);% input matrix initialization u (1) = 0; e = [0 0.868];% deviation matrix initialization c = [0 0];% process matrix initialization kp = 0.803;% PID scale factor ki = 0.282;% PID integration coefficient kd = 0.02;% PID differential coefficient esum = e (2); for n = 3: 1: 100% conventional PID control system for reference y (n) = 1.559 \* y (n-1) -0.559 \* y (n-2) + 0.3 \* c (n-1) + 0.1 \* c (n-2); e (n) = u (n) -y (n);

**Figure 26.** *Intelligent sampling control schematic diagram.*

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

esum = esum + e (n);% deviation summation c (n) = kp \* (e (n)) + kd \* (e (n) -e (n-1)) + ki \* esum;% PID link output y (n) = 1.559 \* y (n-1) -0.559 \* y (n-2) + 0.3 \* c (n-1) + 0.1 \* c (n-2);% c (n) signal drives the differential equation end t = 0: 1: n-1; plot (t, y)% response curve output for n = 3: 1: 100% humanoid intelligent integral control y (n) = 1.559 \* y (n-1) -0.559 \* y (n-2) + 0.3 \* c (n-1) + 0.1 \* c (n-2); e (n) = u (n) -y (n); esum = esum + e (n); edrta = e (n) \* (e (n) -e (n-1)); if (edrta> 0) & (e (n) = 0) c (n) = kp \* (e (n)) + kd \* (e (n) -e (n-1)) + ki \* esum;% for integration else c (n) = kp \* e (n) + kd \* (e (n) -e (n-1)); end y (n) = 1.559 \* y (n-1) -0.559 \* y (n-2) + 0.3 \* c (n-1) + 0.1 \* c (n-2); end t = 0: 1: n-1; plot (t, y, 'r')% response curve output
