## 2 Methodology  
### Fuzzy Logic Control Theory  
Fuzzy logic control was first proposed by [PERSON] who comes from the University of Carlifornia at Berkeley in his paper in 1965 [14]. In his paper, he has elaborated on his ideas in a 1973 paper in which he proposed the concept of \"liguistic variables\". The content of his paper has been equated to a variable defined as a fuzzy set [15]. Fuzzy logic idea is similar to the human being's feeling and inference process. Unlike the classical control theory that is a point-to-point control but fuzzy logic control has approached a range-to-point or range-to-range control. In Figure 1, the certain fuzzy logic control system will have four functional blocks: Fuzzification, inference engine, rule base, and defuzzification. The initial signal is a crisp input data that will be admitted into the fuzzy logic control system. After that, this signal will be converted into fuzzy input data by the fuzzification block. The inference engine or decision making logic will determine how the fuzzy logic control operations are displayed (Sup-Min inference), and together with the rule base will identify the output parameters of each fuzzy control by If-Then rules. The combination will be converted to crispy values with the defuzzification block. The output crisp value can be computed by the center of gravity or the weighted average. The fuzzy control system is based on the rule system that is on expert knowledge and including fuzzy sets and fuzzy algorithms. In summary, from Figure 1, fuzzy logic control process will be conducted following function blocks. The first block is fuzzification and membership functions that understands and characterizes the system behavior by using knowledge and experience. The second block is fuzzy control rule with directly designing the control algorithm with fuzzy rules in which they describe the principles of the controller's regulation in terms of the relationship between its inputs and outputs. The last block is defuzzification to simulate and debug the design. The variables in fuzzy logic system could have the values in range of 0 and 1. Hence, the type of logic system is able to address these values of the variables to set between completely true and completely false. The variables are called as linguistic variables and each linguistic variable is appointed by a membership function along with has a certain degree of membership function at a particular instance. In practice, membership functions have multiple different types, such as the triangular waveform, trapezoidal waveform, Gaussian waveform, bell-shaped waveform, sigmoidal waveform, and S-curve waveform [16]. In order to choose accurate type of membership functions needs to depend on the actual applications. However, in this research case, it needs to have significant dynamic variation in a short time, so a triangular or trapezoidal waveform would be utilized. Whether the performance is not satisfactory then users only modify some fuzzy rules and resume. Fuzzy logic control has been applied widely to the control process through fuzzy linguistic descriptions [17; 18]. In order to process, in detail, the inputs to get the outputs reasoning, there are six works that need to do in applying to fuzzy logic control theory (Figure 2). The first work needs to identify the input factors along with their ranges. This work completely bases on experimental knowledge of fuzzy logic control theory. The second work is also similar with the first work but instead of determining the output factors. The output factors can include one factor or more, but in this study, the output factor only has one factor: diesel engine speed (n). The third work must conduct to create the degree of membership function for each input factor and output factor. The fourth work is an establishment of the construct of rule base in aims with controlling system operates. The next work needs to give the action that will be executed by assigning strengths to rule base of controlling system.