**4. Hierarchical intelligent control and learning control**

Large systems usually have the following characteristics: the high-level order of the system, a large number of subsystems and interrelationships, a large number of system evaluation goals, and conflicts between different goals. People studying complex problems usually deal with them at different levels. Similarly, more complex large-scale system control problems are usually broken down into several interrelated subsystem control problems to deal with. Large-scale complex control systems use multi-level and multi-objective control to form a pyramid-like hierarchical control structure. Aiming at the large system control form, according to the information exchange method and related processing methods, it is generally divided into three basic forms: decentralized control, distributed control and hierarchical control. The main structure of the large-scale system control hierarchy includes multiple descriptions, multi-level descriptions, and multi-level descriptions. According to the number of decision-making objectives, the system can be divided into single-stage single-objective systems, single-stage multi-objective systems, and multi-stage multi-objective systems [17].

Regarding the hierarchical control principle of the aforementioned pyramid control structure, the configured controller receives information from an upperlevel controller (or a decision unit) and is used to control the controller (or a subsystem) at a lower level. The possible conflicts between controllers depend on the coordination of the superior controller (or coordinator). There are many methods for coordination, but most of them are based on the two basic principles of association prediction coordination and association balance coordination.

#### **4.1 Structure and basic principle of hierarchical intelligent control**

The human central nervous system is organized according to a multilayer structure. Therefore, the multi-level hierarchical control structure has become a typical structure of intelligent control. Multilevel hierarchical intelligent control system is a branch of intelligent control. It was first applied in industrial practice and it played an important role in the formation of intelligent control systems. Hierarchical intelligent control structure, according to the intelligence level, it is divided into three levels: organization level, coordination level and control level. Hierarchical intelligent control principle uses the principles and methods of human intelligence, such as human organizers and coordinators, to have the ability to use and process knowledge, and have different degrees of self-learning ability to achieve control purposes. The principle of multi-level and multi-level hierarchical intelligent control of large-scale systems has three characteristics: (1) The more high-level units, the larger the scope of influence on system behavior, and therefore requires higher decision-making intelligence. (2) The decision cycle of the high-level unit is longer than the decision cycle of the lower unit, which mainly deals with factors that involve system behavior and change slowly. (3) The higher the level, the more uncertain the description of the problem, and the more difficult it is to formulate quantitatively. Therefore, the hierarchical intelligent control system is based on the aforementioned principle: accuracy increases as intelligence decreases. Under the unified organization of high-level organizers, multi-layer intelligent control systems can achieve optimal control of complex systems [18].

#### **4.2 Hierarchical learning control system**

#### *4.2.1 The concept of learning control*

Learning is one of the basic intelligences of people. Learning is to gain knowledge. Therefore, learning control that simulates human learning intelligent behavior in control belongs to the category of intelligent control.

Learning control means that if a system can learn the information inherent in the unknown characteristics of a process or its environment, and use the obtained experience for further estimation, classification, decision-making and control, so that the quality of the system can be obtained. Improve, and then call this system a learning system. The learning information obtained by the learning system is used to control the process with unknown characteristics. Such a system is called a learning control system.

The unknown environment in the learning control system includes the controlled dynamic process and its interference. The learning control law can be different learning control algorithms. The memory is used to store the control information and related data in the control process. The performance index evaluation is to control the learning control. The experience gained in the process is used to continuously estimate the characteristics of unknown processes for better decision-making control.

Because there are many ways to implement the learning control algorithm, the composition of the learning control system will also have different structural forms due to the different learning algorithms.

#### *4.2.2 The main form of learning control*

First, adaptive control with learning function. The adaptive control system should have two functions: one is a conventional control function, which is implemented by a closed-loop feedback control loop; the other is a learning function, which is implemented by another feedback control loop composed of an adaptive mechanism, and its control object is the controller itself.

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

The adaptive control system has a learning function, but the structure of this learning is different from the structure of the learning control system. The learning function of adaptive control is the feedback and evaluation of control performance of conventional controllers, and then the parameters or structure of controllers are adjusted or corrected online through adaptive mechanisms, so that the next step of control performance is better than the previous step. Yes, this is learning. It can be considered that the adaptive learning system is a two-level hierarchical control structure composed of a double closed-loop control system. The conventional control loop is a low-level form of a hierarchical structure, which completes the direct control of the controlled object. The second loop including the adaptive mechanism and the conventional controller is a high-level form of the hierarchical structure. The feedback control form completes the learning function of the controller control behavior.

The iterative learning control system and the repeated learning control system do not have a two-level hierarchical structure like the adaptive control system, but only increase the memory to remember the past control experience. The learning in iterative control is realized through the empirical memory of "weighted sum of control action and error" in the past. The assumption that the system is not deformed and the intermittent repetitive training of the memory unit are the essential characteristics of iterative learning control. The memory function of the repeated learning control is completed by the repeated controller, and its correction of the control effect is not realized intermittently but continuously.

Second, learning control based on neural reasoning. In a neural control system where the neural network directly acts as a controller, the neural network actually changes the connection weight between neurons in the network through a learning algorithm, thereby changing the non-linear mapping relationship between the input and output of the neural network, and gradually approaching the controlled dynamics. The inverse model of the process is used to achieve the task of control. This learning of neural networks is different from the learning forms in iterative learning control and repetitive learning control. The former study is based on the idea of approximation, while the latter uses the previous control experience of the control system to find an ideal input characteristic curve based on the actual output signal and the expected signal of the measurement system, so that the controlled object can produce the desired motion. The process of "finding" is the process of learning control.

Third, learning control based on pattern recognition. The first problem encountered when applying the principles and methods of pattern recognition to control systems is how to describe the dynamic characteristics of the controlled object. For controlled objects with a reference model, pattern recognition is mainly used as a signal processing method, but it has no practical value due to the large amount of calculation. For some complex production processes that cannot be modeled or parameter estimated, pattern recognition has become an important means to obtain working condition information and knowledge.

#### **5. Conclusion and future prospects**

In **Table 3**, we introduce the advantages and disadvantages of Artificial Neural Networks, Machine learning control, Bayesian probability control, Fuzzy control, Expert system and Genetic algorithm Control.

Advanced intelligent control is mainly used in comprehensive application scenarios such as computer technology, GPS positioning technology, or precision


input data.

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



#### **Table 3.**

*Development trend of intelligent control technology.*

sensing technology. With the increasingly fierce competition in the product market, intelligent products have obtained good application advantages in practical operations and applications. The main results include greatly improving the operator's operating efficiency, improving the quality of work in some dangerous places, and reducing work intensity, solving application of some dangerous and key constructions jobs, enhanced machine automation and intelligence, improved equipment reliability and reduced maintenance costs, and intelligent fault diagnosis, environmental protection and energy saving, etc.

According to the, the integration is shown in **Table 4** [29, 30].


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



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

#### **Table 4.**

*Development trend of intelligent control technology.*

At present, the neural fuzzy network has its input and output space divided into a checkerboard pattern. Although it is very easy to implement in hardware, as the input and output variables increase, the number of this checkerboard pattern increases. This leads to an unrealistic increase in the number of required memory or hardware, because more training data is needed for better spatial segmentation than the method in the learning process, otherwise insufficient learning will occur. In complex systems, in order to avoid an increase in the number of partitions, it is a research direction to find a more flexible and irregular partitioning method. The implementation of fuzzy controllers with neural networks such as learning capabilities has become a very interesting research area in recent years. However, many neural fuzzy networks with learning capabilities often require expert knowledge before application. In order to improve its performance, it is also the research direction to automatically generate fuzzy rules and adjust the attribution function only from the training data. In recent years, because genetic algorithms have global optimization capabilities, genetic algorithms have become another useful tool. Currently, genetic algorithms are used to adjust the fuzzy controller's attribution function and neural network-like weighting values. The combination of genetic algorithms and neural-like fuzzy networks to accelerate their learning speed is also worth exploring.

Other intelligent control methods such as machine learning control, Bayesian probability control, expert system and genetic algorithm control, etc., or the aforementioned artificial neural networks and fuzzy control, will be from communication technology, manufacturing technology, construction technology, transportation technology and the integration of energy technology and other different levels is also an important task for us to continue to make good use of the advantages of intelligent control methods and eliminate the disadvantages in the future.

*Automation and Control*
