**2.2 Types and controls of intelligent control based on neural networks**

In the control system, the non-linear mapping capability of neural networks can be used to model complex non-linear objects that are difficult to accurately describe, or to act as controllers, or to optimize calculations, or to perform inference, or fault diagnosis, or both Adaptation of certain functions, etc.

Neural network-based intelligent control this book refers to the collective control of neural network alone control or integration of neural network and other intelligent control methods. The main types of control are the following forms.


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

#### **Figure 13.**

*Structure of model reference adaptive fuzzy controller.*

4.Neural network sliding mode control. Variable structure control can be regarded as a special case of fuzzy control, so it belongs to the category of intelligent control. Combining neural network and sliding mode control constitutes neural network sliding mode control. This method classifies the control or state of the system, switches and selects according to changes in the system and the environment, uses the learning ability of the neural network, and improves the sliding mode switching curve through self-learning in an uncertain environment, thereby improving sliding mode the effect of control.

## **2.3 Neural control based on traditional control theory**

The neural network is used as a link or links in a traditional control system to serve as an identifier, controller, estimator, or optimization calculation. There are many ways to do this. Some common ways are summarized as follows.


$$e(t) = \mathcal{M}\_{\mathcal{Y}} \left[ \mathcal{y}\_d(t) - \mathcal{y}(t) \right] + \mathcal{M}\_u u(t) \tag{12}$$

Among them, *My* and *Mu* are matrices of appropriate dimensions. The effectiveness of this method has been confirmed in the underwater robot attitude control. In addition, the combination of neural network and traditional control, as well as endometrial control, neural predictive control, and neural optimal decision control, will not be described in detail.
