**Application to System Modeling and Control Problems**

376 Fuzzy Inference System – Theory and Applications

Yager, R. R. and D. P. Filev (1994). "Approximate clustering via the mountain method."

**18** 

*Spain* 

**Control Application Using Fuzzy Logic:** 

R.M. Aguilar, V. Muñoz and Y. Callero

*University of La Laguna* 

**Design of a Fuzzy Temperature Controller** 

The reason for using fuzzy logic in control applications stems from the idea of modeling uncertainties in the knowledge of a system's behavior through fuzzy sets and rules that are vaguely or ambiguously specified. By defining a system's variables as linguistic variables such that the values they can take are also linguistic terms (modeled as fuzzy sets), and by establishing the rules based on said variables, a general method can be devised to control these systems: Fuzzy Control (Babuška, 1998; Chen, 2009). Fuzzy control is a class of control methodology that utilizes fuzzy set theory (Pedrycz, 1993). The advantages of fuzzy control are twofold. First, fuzzy control offers a novel mechanism for implementing control laws that are often based on knowledge or on linguistic descriptions. Second, fuzzy control provides an alternative methodology for facilitating the design of non-linear controllers for plants that rely on generally uncertain control that is very difficult to relate to the

Every day we mindlessly perform complex tasks: parking, driving, recognizing faces, packing the groceries at the supermarket, moving delicate objects, etc. To solve these tasks (overcome an obstacle), we gather all the information necessary for the situation (topology of the terrain, characteristics of the obstacle such as speed, size, …). With this information and by relying on our experience, we can carry out a series of control actions that, thanks to the feedback present

The controller receives the performance indices (reference) and the system output. To replace the human in a control process, a controller must be added. The controller is a mathematical element, and as such all of the tasks that it is able to perform must be perfectly defined. This control link is studied in Control Theory and is based on two principles:

1. The system to be controlled must be known so that its response to a given input can be predicted. This prediction task requires having a complete model of the system. This

2. The objective of the control must be specified in terms of concise mathematical formulas

When a system's complexity increases, mathematics cannot be used to define the aforementioned points. The model cannot be defined due to non-linearities, to its non-

identification phase is essential to the performance of the control algorithm.

directly related to the system's variables (performance index).

stationary nature, to the lack of information regarding the model, and so on.

conventional theory of non-linear control (Li & Tong, 2003; A. Sala et al., 2005).

between the system under control and our bodies, can achieve the desired goal.

**1. Introduction** 
