## 1 Introduction  
Fuzzy logic control was proposed by Prof. [PERSON] in 1965. The Mamdani fuzzy inference system was displayed to conduct a steam engine and boiler control process by linguistic rule [1; 2]. On the other hand, fuzzy logic control has been expressed by if-then rules with the human language. Besides that, the mathematical model does not need to establish in design process of a fuzzy logic controller. So, the fuzzy logic control is a good robustness. Its advantage is easy application, because there is no need an intensive information technology programmers and it has been used widely in science and industry. The rules and membership functions of a fuzzy logic controller are definitely based on empirical experience or knowledge database. Fuzzy logic control has been reported to be successfully to use a number of complex and nonlinear processes. Fuzzy logic control is occasionally proved to be more robust and their performances that are less sensitive to parametric variations than conventional controllers [3; 4]. [PERSON], et al., have analyzed the fuzzy control rules and membership function parameters [2]. [PERSON], et al., 2009 have compared between rigid feedback and constant-speed feedback control system, and construct a fuzzy control system of diesel speed. This comparison will result that the fuzzy control method is a feasible method and better than the other methods [5]. Some other researches of scholars and scientists have been indicated the benefits to apply fuzzy logic control in optimizing service system of diesel engine. [PERSON] and [PERSON]' have established fuzzy modelling and control system of the air system for diesel engine [6]. [PERSON]. [PERSON] et al. have integrated governor control for a diesel by the detailed implementation of real-time fuzzy logic speed control for a standby diesel-generating set [7]. [PERSON] et al., had established the diesel engine rotational speed automatic controller by using fuzzy-PID logic control theory [8]. On the other hand, the study of fuzzy logic theory has also conducted through some articles. [PERSON] et al., had modeled the uncertain nonlinear systems along with fuzzy differential equations (FDEs) by fuzzy set [9]. Moreover, the determination of uncertainty property through Z-number of fuzzy equation has also been investigated by [PERSON] et al. [10]. Furthermore, Particle Swarm Optimization (PSO) algorithm has been used to get the optimal values from fuzzy logic control rules. PSO has been based on a metaphor of social interaction. PSO's function conducts to look up a space by adjusting the trajectories of individual vectors and called 'Particles'. The movement of points in multidimensional space is conceptualized by Particles. The positions of their own previous best performances and the best previous performance of their neighbors are determined stochastically by individual particles. One another side, two notable improvements have been referred as the initial PSO, which attempt to strike a balance between the two conditions. The first condition is introduced by [PERSON] and [PERSON] which has been used like as an extra 'inertia weight' term and to scale down the velocity of each particle and this term is typically decreased linearly throughout a run [11]. The left condition has been introduced by [PERSON] and [PERSON] involves a 'constriction factor' where the entire right side of the formula is weighted by a coefficient [12]. On the other hand, the combination between fuzzy logic control and PSO has been conducted by some researches at individual field. [PERSON]. [PERSON], et al. have introduced evolutionary methodology for combining Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs) when using fuzzy logic control for decision making [13]. In this study, the Particle Swarm model allows an infinite number of ways in which the balance between exploration and convergence can be controlled. Particle Swarm Optimization algorithm is studied to choose the membership functions and the fuzzy control rules.