*1.3.1.3 Principle of adaptive fuzzy controller*

The adaptive fuzzy controller also needs to understand the parameters of the controlled object while controlling the controlled object. Therefore, it is actually a control method that combines fuzzy system identification and fuzzy control. Through identification, we can better "understand" the controlled object so that the controller can "follow" changes in the object and the environment. In this way, the controller itself has a certain ability to adapt to changes, or the adaptive fuzzy controller has higher intelligence.

The three function blocks added by the adaptive fuzzy controller are implemented by software to implement their # functions. The adaptive link can be understood as the introduction of a "soft feedback" inside the fuzzy controller, that is, the feedback of the controller's own performance implemented by software. Through this feedback, the control performance of the controller is continuously adjusted and improved to make the control effect of the control process is sent to the best state.

The above method is still feasible for a system with a single input and single output and which is not critical to the calculation time. The relationship matrix for a multiple input multiple output system is too large for a computer to store and compute.

#### *1.3.2 The principle and method of model reference adaptive fuzzy control*

#### *1.3.2.1 Basic principle of model reference adaptive fuzzy control*

The model reference adaptive system originates from the concept of selfadaptation of human behavior and causal reasoning (law of cause and effect) being transplanted into the field of control. The causal reasoning model is a general model of the reasoning process that expresses human adaptive characteristics. The causal law model characterizes the qualitative relationship between cause and effect.

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

**Figure 8.**

*Structure of model reference adaptive fuzzy controller.*

By comparing the model with the real situation, people use adaptive mechanisms instead of people to modify parameters or control strategies to obtain a process that is close to the desired output for control system.

The basic structure of the model reference adaptive fuzzy control system includes three components:

