Fuzzy Logic Control with PSO Tuning

*Jeydson Lopes da Silva*

### **Abstract**

Several applications of artificial intelligence in the area of control of dynamic systems have proven to be an efficient tool for process improvement. In this context, control systems based on fuzzy logic - Fuzzy Logic Control (FLC) are part of a series of advances in the areas of control systems. Fuzzy control is based on natural language and therefore has the ability to make approximations closer to the real nature of the problems. The use of metaheuristic algorithms such as the particle swarm optimization (PSO) allows it to provide adequate adjustments to the fuzzy controller in an optimized manner. This technique allows to adjust the FLC in a simple way according to the performance desired by the designer, without the need for a long time of conventional tests.

**Keywords:** FLC, PSO, artificial intelligence, controller, optimization

#### **1. Introduction**

FLC represents a family of intelligent controllers with a lot of potential for use in the world for industrial control systems. Its popularity is mainly due to its performance being robust in several operating conditions and its functional simplicity, in addition to its ease of implementation, allowing engineers to operate them in a simple and direct way. Even with the emergence of new control techniques, FLC controllers will still be on the market for a long time in industrial plants [1].

A good parameterization of the FLC inference functions is essential to allow a good performance of this type of closed loop controller. The tuning of the controller is a persistent problem in the area of control and automation, from the initial approach of this topic to the present day; a definitive solution has not yet been reached, being a subject constantly addressed in several works in the field of control engineering. However, it is important to note that despite the various techniques that produce adjustments in the FLC parameters, it is still necessary to assess the designer regarding the result of the parameterization of this controller [2].

In recent years, the computational capacity available allows optimization techniques developed in the field of artificial intelligence to gain space in the solution of several engineering problems [3, 4]. In this context, algorithms based on metaheuristics can provide adequate solutions to the FLC parameterization problem. Since the parameters necessary for the proper functioning of the FLC can be numerous and often complex, techniques based on intelligent computing provide an alternative solution to this type of problem.
